Category: DevOps Practice

  • Splunk: Creating a Resilient and Dynamic Organization

    Splunk: Creating a Resilient and Dynamic Organization

    Mitch Ashley: I have the pleasure of being joined by Ryan Kovar, Ryan is a distinguished tech security technologist and leader of SURGe with Splunk, and Cory Minton who’s field CTO for The Americas with Splunk. Welcome, guys.

    Ryan Kovar: Thank you.

    Mitch Ashley: Good to be chatting with you both. A topic that’s very top of mind for CISOs or IT leaders, of course, is how do we create a resilient and dynamic organization that can keep pace with the business and the change that we see in the technology landscape, whether it’s our own infrastructure, own application portfolio, cloud, et cetera, all of those things, but also, of course, the tax services that are evolving and changing as what the bad guys, the threat actors, are doing. We’ve also not only got to have a great and resilient technology stack, but organizationally, process-wise, all of those things have to fit together into a cohesive strategy.

    We’re here to talk about that, thinking about it as an IT leader, whether you’re in security or IT or combination of both, and discussing your all expertise experience, but also you talk to a lot of customers as leaders in Splunk and the kind of things that you do. Cory, it’d be great to have you kick things off. Maybe we should start with if you want to start with the people domain or maybe you want to set it up a little bit differently. I’d love to hear your initial thoughts on this.

    Cory Minton: Yeah. No people’s perfect. I think the people process technology lens on tackling any sort of problem for leaders today is an appropriate framework. I’m happy to talk about the people portion of building great cybersecurity and IT organizations that, like you said, deliver resilience.

    Ryan Kovar: For me, at least when we think about this, you said IT leaders, and I think one of the big changes I’ve seen across for cyber for CISOs is it’s no longer IT or security. It is business leaders. When we started talking about people, that to me starts resonating because that is a cognitive change in how CISOs think of themselves and they think of their value, which we often get stuck in the technology part of people process and technology because a lot of us are technologists at heart, but I think the big change that Cory and I have seen when we start talking to CISOs and CIOs is this convergence of skills and the need to support the business differently.

    Cory Minton: Yeah, and it’s an interesting sort of people market, too. I think leaders have to think about the fact that, yeah, in technology, there has been some turmoil in some of the big tech companies, but there’s still a lot of really great talent out there. I think that there’s choice. With unemployment so low, and there’s, what, like 16 million jobs unfilled currently in the US and a lot of those being in the tech sector, people have choice and they have a place where they can go.

    I think, leaders, if they’re going to build a talent pipeline and build a great organization, they have to find folks where they are and bring them in and connect them to a value, a mission and a vision that they get excited about. Certainly, securing digital services and protecting against bad actors is exciting in and of itself for many folks, but actually connecting those people to how it affects the outcomes that matter to the business and that whole value creation process, I think, is a real critical skill for folks to understand today as they, again, are out communicating and interacting in the network, building networks of talent pools. I think that clear vision of why it matters and why the work that somebody would do with you matters is critically important.

    Ryan Kovar: That reminds me of the cliche, of course, that people don’t quit jobs, they quit leaders. When you flip that around, one of my mentors, Susan St. Ledger, told me, you can evaluate the success of a leader by how many people follow them to another company. I know right now, in this market, what I’m finding is people are staying longer because they like the people they work for or they like their leadership team, they like the culture. The money, even if they’re not making as much as they were last year because of inflation, the people I know who stuck around longest are the people happy with the people that they work for and work with.

    Mitch Ashley: It’s interesting when we talk about people. I’ve worked in part of my career where the sort of higher the purple unicorn that doesn’t exist, that got eight skills, that needs 20 years of experience for things that have only been around for three. We set our subs up to hire these phenomenal people of which there may only be a few in the world that are like that, but it’s about growing our people, but also growing our organizations, developing them. It’s not just hiring people. It’s building a team. It’s building an organization that’s got the right talent at the right time at the right place to match with the business needs, and that changes. That means you can’t always hire for it. You’re partnering for it. You’re working with companies like Splunk to bring in skills and expertise maybe you need for the moment, you need for a project, you need for strategy, all of those things. Love to hear your perspective about broaden what we think about people and how we incorporate that part of it into our strategy.

    Cory Minton: Yeah. I’ll say, top to bottom, start at the executive level down to practitioners. Everybody has a partner. Everybody has a consulting partner typically that they bring in to, like you said, fill those gaps where it’s a new skill, it’s a new technology, it’s a new capability that they’re trying to deploy for the reasons that they’ve chosen to do so. They’re valuable, but you may not have those skills internally. Training takes time and, oftentimes, just finding that partner that can help you solve that particular problem is incredibly important. I think, as the security landscape and, frankly, the tools being used to deliver and develop the latest digital services continues to get more complex, expecting to hire that skillset completely, probably not realistic especially as digital transformation objectives have an ebb and a flow of momentum and the amount of work being done on particular projects.

    Yeah, I see it as one of the top areas where, when I’m talking to CIOs and CTOs and CISOs, when they think about, “Hey, we want to deploy this new capability within Splunk,” or, “We’re, frankly, trying to figure out how to integrate better with our observability and IT teams to derive more value from the tools we’ve already purchased,” it’s oftentimes that people conversation of, “How do I actually leverage your talent to show us what good looks like and bring the experience from other companies similar to us that have already been down this path to, call it the cheat code, how do I do the thing that you guys already know how to do? How do I buy that capability?” Oftentimes, it’s through people.

    Ryan Kovar: My experience, I’ll go the other side of the halo and horns there down to just the nitty-gritty hiring. Recently building out my team, I intentionally carved out three slots for entry level, early career folks into our cybersecurity team, which is pretty rare for Splunk and pretty rare for a lot of security research teams, but the way I did that was really rewriting the job description to be embracive of second-career people. I actually really dislike the term “there’s a problem with the pipeline for hiring”. I think that’s completely false. The problem is not in the pipeline. The problem is we have a valve on incredible talent and gate-keeping who actually gets into the pipeline. We do see a problem with the pipeline when I start looking for folks who are underrepresented in cyber especially 15, 20 years down.

    What I don’t see is a problem of, basically, the reservoir. The reservoir is healthy. The reservoir is huge, but people either don’t feel like they’re welcome or they self-select out especially of cybersecurity. For me, a lot of the people problems that we have, if we can start working on the problems of today, future us, as a security leader, will be very happy if present me can help unlock that reservoir by turning open the valve by creating job descriptions and roles that are less around years of experience and understanding technologies that I can Google or generative AI my way through today.

    I think there’s just a lot of flexibility especially for folks who have critical thinking and communication skills. I can teach you TCP/IP. I can’t teach you how to communicate. I can’t teach you how to synthesize information. I need you to walk in the door with that. Something for me around people and these hiring gaps that we see is really about being more embracive of nontraditional cybersecurity and IT roles and then also facilitating them in ways they can succeed.

    Mitch Ashley: Well, and it’s also about hiring for what we need today, but also where the ball is going to be downfield when the ball lands. Part of that is hiring people who have demonstrated their ability to learn, adapt. Yesterday, they were TCP/IP expert. Today, They’re the security threat landscape expert. How did they do that? How did they get there? They learned it. It may have been on the job, but there’s also a lot of self-motivation and just skill in that learning, and that repetitive learning skill is I think something you can harness or leverage to accelerate their career as well as what you need.

    Cory Minton: Absolutely. It’s one of those things that, always, some folks get turned off. As Ryan said, sometimes there’s a valve, and one of those valves is like, “You must have a college education.” While I don’t necessarily believe that a college education is always the right thing, I think what it proves if you have one is that, as you said Mitch, you’re able to go through a structured learning process in an organization that’s institutional, understand dynamics and things and achieve an objective which was set before you that had some measurable outcomes that you had to deliver, which I think is something true for all of us in a corporate responsibility job as we have to operate in an institutional environment. We have to consistently learn new things. We have to interact with people around us. I think college is a good measure, but, as Ryan said, oftentimes, a second career is maybe even a more powerful measure of somebody who’s done that successfully.

    Like Ryan said, the technical skills are learnable and, frankly, the fun part is some of the technical skills are actually getting obfuscated by advancements in technology. As we think about things like, Ryan, you mentioned, generative AI, we can make the joke, but, candidly, wouldn’t you rather hire somebody who’s got incredible cybersecurity skills and understands the landscape more so than somebody who’s just really good at crafting queries because then, if you have somebody that has that domain expertise as technologies like generative AI and other sort of assistive technologies continue to evolve, then the domain expertise becomes the most important because then you start interacting on a natural language way and you don’t have to have those same technical skills to get there, which I think is probably one of the most interesting uses for generative AI is actually bringing out the barriers for technical ability to execute in a job like security or IT.

    Ryan Kovar: Prompt engineering will be one of the most significant requirements for entry-level and mid-level jobs by next year, in my opinion, categorically.

    Mitch Ashley: We’ve already been through one generation. It’s called search engines. Now, we’re doing it with generative AI.

    Ryan Kovar: Yeah. Yeah. No. We’re talking about this, but the reality is I’ve been doing cybersecurity and IT since 1999. Cory, there’s enough gray there. I’m sure you’re about the same generation. When I started, there was no Google and you had to read the Microsoft TechNet documentation, and then there was Google, and I put 65 CD binder in the trash and said, “Never again.”

    The fun thing that I always tell people that I’m mentoring or advocating for in cybersecurity is I’ve been doing this for 24 years and, of the 24 years, I have about four years of knowledge that’s relevant. I have 24 years of wisdom, but four years of knowledge that’s relevant. Everything else, no one cares that I can fix Exchange 5.5 driven pub EDB and I know how to defrag a Windows NT 4.0 server. That doesn’t matter. That’s one of the great things about cybersecurity and IT in the general is that you can become a subject matter expert in something very quickly and not have the bias of age.

    Mitch Ashley: Well, let’s turn our lens to the process part of it, and we’ve talked a lot about people and we could talk a lot more about it. There’s some great conversations that we’ve had already about that. We’ve got to have processes that are well-oiled to highly tuned, but can be responsive of incident management when things happen. The organization now has to operate cross-functionally, the stove-pipes, the things that we’ve lived with for so long. Now, we have to operate cross-functionally. We’re trying to tear those down, but our processes have to work across those.

    I’m interested in your all thoughts about how are organizations, what do they best do to adapt to what we need today so we can be speedier, respond more quickly and more reliably to threats or incidents or needs of the business?

    Cory Minton: Yeah. Do you want to to hit it, Ryan?

    Ryan Kovar: No. Please, Cory, go ahead.

    Cory Minton: Okay. I was actually going to say it’s actually not even an option anymore. We talk about that they need to do it. It’s not an option. If you look at some of the SEC’s recent rulings on the disclosure of material and incidents that happen for publicly traded companies in the US, you have to report that. You have to have that cross-functional view of how if a cybersecurity incident happens or an IT sort of incident outage or breach, any of these categorical things that happen, if they have a material impact on operations that would affect shareholder value, then you must disclose those things, and so now the impetus is on every publicly traded company to get this figured out really well. It’s got executive buy-in now that we’re going to tear down the walls between security and IT operations and our engineering teams developing our next digital capabilities because, anywhere across that spectrum that we have a process breakdown, whether it’s externally caused or internally caused, if it’s material, we better report it.

    These are no longer like, hey, it’s a good idea. This is like “test that theory and report the results” kind of stuff that the SEC will send somebody to jail over. We’ve already seen some convictions on previously publicly traded companies on misreporting things. It’s no longer a game. We’re serious about this. I think, from a process perspective, one of the things I’m seeing is reaching across the aisle. CISOs are talking to CIOs and CTOs more so now than I’ve seen in the last couple of years because they have to understand those impacts and they’re looking to organizations that actually already do some of them within the company that are already being trusted by different pockets of the organization. They’re looking externally for that guidance and help, whether it’s from consulting partners that are, hey, advising on security operations or advising on software development capabilities or technology partners.

    I mean, even the CISA, the Cloud Infrastructure Security Agency, their recent strategy update talked about one of the key pillars of their annual strategy for resilience included technology partners that were going to help them achieve that resilience. I think we have the impetus. The measurement is required now. There’s no longer a game, and I think executives are starting to understand it and they’re looking external to say, “Who can help me deal with this?” No matter where we land on what is material and some of the legal questions on how it’s implemented, we still have to respond and we still have to report some of those capabilities. Now is the time to start reaching across the aisle and starting to ask questions of your partnerships that you have in the org on how can you help us solve this problem.

    Ryan Kovar: I look at things like DevOps which still today has a little bit of a carve out separate. In a lot of organizations, DevOps is a slightly different place than maybe traditional security and, well, certainly than security, and then possibly even different than traditional IT engineering or infrastructure. In today’s society or today’s world of technology, a lot of the security issues that organizations are facing are in their DevOps pipeline, and so I find it fascinating what Cory said. The SEC has this term, material finding. You have to report a material finding.

    Now, I as a security professional may very well know what that is, but, frankly, the DevOps world has gone feral to a point where the security organizations are not a part of it and so the DevOps team are the only ones who understand what the security implications are. They’re the ones finding it and, because they’re in a DevOps mind frame, they’re not stopping and saying, “Let’s create an incident. Let’s walk this through.” No. They’re just fixing it and they’re moving it on. That’s how the cloud works. That’s how DevOps works.

    There is this essential need for us as an industry to really start reaching more across because we are being outpaced by DevOps home growing their own SecOps without oversight, without the wisdom of the security world, but we can’t stay in the way of the business, which is why that’s so important for us to go across and understand this because now there’s regulatory requirements and the real world is people are going to move forward whether they like it or not, so get on that train.

    Cory Minton: Yeah/ it’s funny, Ryan, you say that. I was reading the SEC reports from JPMorgan Chase. Jamie Dimon, their CEO, said in the letter to shareholders he had two sort of funny juxtaposed statements that used the same phrasing, which I found interesting. He said two things he could not overemphasize, one was, “I cannot overemphasize the need for cybersecurity in our organization. Everything must be secure,” and then later in the letter he said, “I cannot overemphasize our need to deploy innovative technology,” which is exactly what you said. If my CICD pipeline is continuing to push updates, but I’m not applying those cybersecurity principles that, like you said, was incident review and forensics and actually looking into it, then we’ve missed the boat on this. One of the key parts of this SEC filing is you not only have to report the outage, but you actually have to from an annual reporting perspective talk about your processes that you’re using to ensure cybersecurity in those areas. That’s one of those that’s like the CEO is saying this to shareholders. We better pay attention.

    Ryan Kovar: The CEO of a bank that touches 20% of every dollar in the world every day, right?

    Cory Minton: We cannot overemphasize.

    Ryan Kovar: I love that example because I look at DevOps as a perfect world where I have no fear at all that the DevOps team will identify and remediate security issues very quickly and then continue to do so because they’re not looking at the larger picture. Why did this get ingested? Why do we have this happening? Why is this CRO a threat actor you may not know anything about a threat actor. You may not understand that Tampa strawberry as per Microsoft has a really significant interest in your organization, and one of their TTPs is actually moving up the chain and actually jumping ahead for software supply chain.

    That is something that I would not expect a DevSecOps engineer to understand, but that is where the context of a larger security world, and that’s why this resiliency message, this is why it’s working across aisles. It becomes so important for the process. I honestly think the technology is usually the easiest part of this. The people and the process to implement and secure the technology, way harder, way less interesting than most people, except nerds like me, but much harder.

    Mitch Ashley: Well, and what you really bring to light is that the security specialists are going to understand the threat landscape much better. DevOps developers, et cetera, know their environment focused on the things that they are. You mentioned Jamie Dimon. Those two things, he’s expecting us, our CEOs are expecting us to figure out how we bring that together, how we make that happen so we don’t have feral organizations, so we don’t have processes that are brittle when things really fall apart or our environments get more complex. I mean, that’s an obvious thing. They are complex and they’re getting more complex, and no one understands the whole thing, so we’ve all got to pull together and say there are three dimensions to this problem, not one, and here’s the steps. We together figure out how we fix this.

    Great. Well, let’s talk about the technology perspective then. Obviously, Splunk being a great technology company, have been around for a long time. I remember finding Splunk on the showroom floor in the early days still doing black T-shirts then just like they are today, a great company that you all work for. Let’s talk about some of the technology side of what’s important and, again, thinking about not just internal people and processes, but also third parties that we’re using like the Splunks of the world in our organizations and how you can help with this challenge.

    Ryan Kovar: I’ll start really easily on this one, and then Cory will actually say something much better. People buy software to solve their problems. That’s it. That’s the easiest way when I pay people. I didn’t come from a sales background. I was operational. I was a threat hunter for the government, threat intelligence at DARPA, places like this. I didn’t think about why I bought software until I worked for a software vendor. It’s very easy. You buy software to make your life easier, to solve a problem faster or to solve it better.

    When I look at what Splunk does, a lot of our recent efforts are really around this incremental growth of just making people’s lives better, making people’s lives easier, cutting down the barriers to do their job faster because that’s what people need, whether it be security, whether it be observability, whether it be traditional IT engineering, that to me is really what we focus on here at Splunk which is why I’m still here after nine years. We make people’s lives better and we allow them to fulfill the mission that they’re actually being paid for rather than fighting the software that they’re buying and paying money for. That to me really is at the heart of what we do.

    Cory Minton: Yeah, and I think, if anybody’s seen some of the Splunk messaging in the market, we’ve really rallied behind this idea of resilience, and I think it actually sums up nicely the things that we do which I think, based on the conversation we’ve just had about the people and process, things that are challenging organizations, I think we’re in a really unique place to be a partner, to be one of those partner organizations and be the software that people buy to make their lives easier because we sit in this unique place of being one of the few organizations that actually unify, tear down the walls between security, IT operations and those feral DevOps teams that are out there building the next digital services because our corporate mission is around resilience, and we think of that as a simple statement of, “How do you keep all this digital business secure and up and running?” Simple statement, but, secure, not a simple thing to do, and up and running oftentimes equally on simple thing to do.

    Ryan Kovar: Sometimes at loggerheads.

    Cory Minton: Yeah. Exactly, a pearl of either sort of objective. There used to be this term we called a MOM. We all love MOM, monitor of monitors. It sits above. It sits at an enterprise level, at a higher level to give you visibility. I think that’s what folks have maybe struggled with that we see. Hybrid cloud is reality, right? There’s lots of clouds, not one cloud. Clearly, there’s leaders in terms of revenue, and each quarter they grow at different paces, but candidly, even Michael Dell said some years ago, “The cloud is not a place. It’s more of an operating model.” As you see SaaS being part of cloud journeys, yes, data centers are still run by organizations. They just are run now in a much more orchestration pattern similar to cloud providers, and that reality of lots of silos across different landscapes is creating challenges.

    If you use one tool that’s provided by said provider, then, yes, you can secure and keep up and running that one particular cloud real estate, but what about how it’s connected across the organization? How about the ways that applications traverse deployment centers? I think that’s what we really see as interesting is organizations are leaning on Splunk especially in the macroeconomic conditions where everybody’s focused less on growth and more on profitability, so they’re looking at like, hey, how do I reduce the number of tools?

    Ryan Kovar: More out of less.

    Cory Minton: Yeah. Exactly. Do more with less. They’re looking at it and going, “Wait a second, so I have this network environ, this router, so my data center. I’m sending the logs from that thing to nine different tools. Why am I doing that?” That’s nine times I’m paying for that bit of data to be stored and processed and analyzed by some number of tools, and organizations are looking at it and going, “Maybe that’s not smart. Maybe that same piece of data has nine questions being asked of it, and is there may be a fewer number of tools that could answer those nine questions effectively to give me the outcome without having to have all this sprawl?”

    It’s a unique conversation that we’re in today. Candidly, the tools consolidation, that rationalization of tools is one, looking at data. The data deluge hasn’t stopped. As we talk about sending data to nine different places, well, as that data becomes richer, it’s no longer just logs. Now we’re looking at metrics for real time. We’re looking at traces for spanning. How does an application actually impact multiple sort of environments? The data is getting rich and large. Is it all equal? Should we all be sending it to the same place? Should we be treating it with some valuation?

    Those are conversations that I’m having today that technology is helping solve, and Splunk is doing some really interesting work in the data rationalization and in that tools consolidation area that’s making it easier for customers to do more. As Ryan said, you bought a piece of software to make your life easier. We’re trying to go out and help folks figure out how to make more lives easier with the software they already bought.

    Ryan Kovar: I’m just required to hit all the buzzwords, so I’ll say ransomware now. It’s fascinating to me. Resilience, as in the concept of business leaders and cybersecurity, CISO leaders having to come together, I think, partly is really being driven by this threat of ransomware because the first time a cybersecurity threat has consistently and continually impacted every aspect of a business where 10 years ago, 15 years ago, oh, APT1 from China, exfil data. Well, that was the big issue. Well, by the time you heard about in the news, it’s already done. It’s actually relatively small scale. The impact is we were compromised, there was a breach, and now we have to deal with the public relations fallout, the stock drop, and also we have to make sure this doesn’t happen again.

    With ransomware, it’s actually we’ve encrypted your critical business systems and you can no longer do business, and now you have questions of do we pay the ransom? Well, now you need to involve the finance or you need to involve your chief legal officer. Oh, it’s publicly out there that we have this security issue. You have to involve the PR team, the public relations team. Oh, it’s actually knocked out our core business. Oh, well, now you’re involving the sales org. Now you’re involving the IT org. It’s coming across all these different places, so it’s builds back into what tools do you have that facilitate this and then, going back to the people part that we touched on earlier, this is where someone needs to be across all of them because they need to understand the impact and actually drive those changes that are needed and, because of ransomware, we actually have to deal with it. We can’t just pretend it doesn’t exist. CISOs can’t just be nerds in the closet playing cyber. They actually have to be able to speak eloquently to their business leaders, to the board of directors and others.

    Cory Minton: Yep. I’m just going to jump on the bandwagon of buzzword bingo. One of the things we’re seeing a ton of, too, is we talk about new digital workloads constantly being brought in, new innovation, we’re hiring consultants to bring in and actually help generative AI get real in our organizations, there’s a lot of conversation about is generative AI threat versus risk? There’s a whole kind of conversation there, but one that actually, as we’re moving past the hype and the AI washing nonsense that’s happened in the market, once we’re getting to real, a lot of CISOs have been talking about or talking to are trying to figure out how do I deploy those technologies internally? How do I go find the models that I can train against my data sets that would actually go help my teams do their jobs better?

    What I would say is is that’s another one of those areas where, as you’re innovating and bringing in new technology, treat it like another digital workload. You’re still going to have to detect when something goes wrong. You’re still going to have to investigate when something goes wrong in that generative AI hallucinates, it has an outage, whatever, and you’re still going to have to respond to those outages and take those learnings and put it back into the operational process of how do you run that system more effectively.

    I think people need help oftentimes finding those consistent patterns that we have to deploy to just keep things securely up and running regardless of what the workload is under the covers, and I candidly see that as one of the greatest areas for leaders that are looking to partner with a professional services organization or an assay, those outside consultancies. There’s a lot of value there in having them help bring in the patterns especially on proven technologies to short cycle, increase your time to value or, excuse me ,shorten your time to value when thinking about those new digital workloads and making sure that they’re secure and up and running.

    Mitch Ashley: I like where you’re taking this, which is one I wanted to wrap with, which is our organizations are expecting us to innovate, not just ourselves innovate, but also support the rest of the organization to be able to innovate. You talked about Ransomware. The newest thing is they’re not encrypting your data anymore. They’re just threatening to release it, so it’s not a matter of losing the data. It’s losing control over it. AI is the next, maybe it’s the next boon to phishing. We may not be able to tell the difference between an email from a generative AI LLM that’s been trained on how our CEO talks versus a well-scripted, predefined email.

    These new kind of threat models as well as new technologies force us to innovate and find new ways to do things. We have to help our people with tools, technologies, processes. I’d love to hear some more thoughts about what do we do to set ourselves up for success to be able to innovate in a way that’s going to help support where the organization is today and where it’s going?

    Ryan Kovar: Well, I’ll make a slightly controversial opinion here. I don’t think there’s ever been a single technology that outweighs the benefits for the offense or defense. I look at generative AI as something that is going to liberate security organizations from the shackles of mundanity. It’ll democratize what we can do in terms of analysis. It will also do the same for the adversaries. The only people who will lose are those who are not taking advantage of this technology on either side.

    I really do believe that we can all up-level as we go through with different technologies just as the adversaries are. When we invented a trench or trench warfare in World War I, they invented the tank. When we invented an airplane, they invented anti-aircraft. This is a tit-for-tat thing, and there’s always a leap at some point. People created a star fortress and that stymied invasions until someone invented the cannon. Sometimes, defense beats offense and sometimes offense beats off defense, but it goes back and forth as you go.

    I don’t necessarily see anything that’s a semantic change in how organizations will defend, but I do believe, if you’re not paying attention, if you’re not innovating, if you’re not selecting vendors, if you’re buying software, if you’re not buying vendors who aren’t innovating, you’re going to have a very difficult time in the next five years because your adversaries absolutely are.

    Cory Minton: Yeah, and I would go maybe less digital and say we as leaders cannot underestimate and cannot undervalue investments that we make in our people and our business culture today that create space for the learning and the development and the skills attainment that Ryan talked about, being critical. Leaders must create space for technologists, for practitioners, for leaders to go learn and to go study and have a mantra of spending some large amount of time on a regular basis, on a cadence basis, investigating, whether that’s going and attending conferences and networking and starting to understand what’s happening in the industry or it’s partnering with a technology organization and doing more hands-on workshops and learnings or it’s going next, when you’ve got a consultant in, spending time with those folks in the office picking their brain on what are the skills that they’re paying you for? Why are you here? What is so unique about what you’re doing, and how do I learn more of that?

    It comes from leaders making that a priority. If we’re going to have an organization that has people, process and technology that’s actually going to be resilient, that’s going to be able to survive in this next epoch of technology innovation, it’s going to have to be led by the best intelligence, which is still human intelligence, and that requires investment, and I think you got to put a stake in the ground and make that a priority today.

    Ryan Kovar: I love that part because, going back to our previous discussions about leaders who often have 10, 20, 30 years of experience, none of it is relevant. When I started my career as CISO, my only half, well, first off, CISOs didn’t exist. Second, the CTO or the person in charge of technology at that company had to pay attention to desktops, printers and servers on-prem, and then 10 years ago, 15 years ago, it was laptops, desktops, printers on-prem, printers in the home offices, a data center that they co-located to in this little cloud thing that they’re trying out, and then five or 10 years ago, it was all those plus software as a service, and now it’s, okay, well, we have a CICD pipeline and we’re driving APIs and our entire business is dependent upon this one piece of open source software that’s maintained by three guys in a former Yugoslavian Republic and, if it has an issue, we’re going to be done.

    Also, we M&Aed a company out of China. Every time they develop a vulnerability, that has to go to the Chinese government along with us. It’s a lot more complex. For those who are just resting on their laurels of “I’ve been doing this for a while” and not up-leveling their own education and hiring people who are subject matter levels as direct lieutenants, you’re going to have a bad day.

    Mitch Ashley: Bringing it back to the thinking that got us here is not the thinking that will get us to the next place, I’m paraphrasing Einstein here, but it’s also our own thinking. That’s the value of bringing in perspectives like yours with your partners, your suppliers, companies like Splunk, professional services. There are a lot of ways. I think we’re at the same junction point where we were with the cloud. Where we’re like, “Here’s the cloud. Is it just somebody else’s computer or is it a new way of doing things?” But what is that new way?

    We’re kind of in that place today with AI. Okay. Right. We know we can do generative AI stuff, but what does it really mean? How do we leverage it, to your point, Ryan? You have to be a participant on the field to really figure that out. You don’t have to go spend crazy money on the latest thing just because that’s what was in the airline magazine, but it is about figuring out and learning what you can do with it and what’s possible learning by yourself, but also with others.

    Well, thank you to both of you, to Cory Minton and Ryan Kovar, for joining us, and thanks to the Splunk team for bringing us together to have this conversation. We hope it’s been really beneficial to everybody who’s listening in. I know it’s been great for me as well. Thank you.

  • Should You Measure Developer Productivity?

    Should You Measure Developer Productivity?

    A recent McKinsey article entitled ‘Yes, you can measure software developer productivity‘ has got the software developer community talking. And the general consensus is they’re not happy.

    Most software developers are not enthusiastic about the prospect of having their individual performance metrics monitored. “Anyone who attempts to measure software in some sort of scientific way is just doing the wrong thing,” said YouTube personality ThePrimeTime. Dave Farley of Continuous Delivery even goes as far as to call the McKinsey report “harmful dribble.”

    This debate arises as developer productivity engineering (DPE) is gaining traction as a means to improve developer performances and, perhaps, in response to employment landscape shifts like the Great Resignation, which saw developers and DevOps pros leaving their roles. Companies like Spotify, LinkedIn, Netflix and others are also investing in internal developer platforms (IDPs) to increase developer productivity and satisfaction amid a limited talent supply.

    Alongside these ambitions, much has been written about how managers can measure developer productivity in terms of qualitative metrics, like developer satisfaction, as well as quantitative metrics, like number of commits or story points, deployment frequency or failure rates. But can tracking such metrics deliver tangible results? Or do they go too far?

    Below, we’ll consider whether you can actually measure software developer productivity. But more than that, we’ll evaluate whether this is a meaningful tactic with desirable outcomes.

    Ways to Measure Development Productivity: DORA

    First, let’s look at some of the standard techniques to measure development productivity. The DORA metrics, produced by the DevOps Research and Assessment (DORA) team at Google after years of research, identified four key metrics:

    1. Deployment frequency: How often you ship changes.
    2. Lead time for changes: The time between a commit and it reaching production.
    3. Change failure rate: The percent of releases that fail; downtime, worsened service or rollbacks.
    4. Time to restore service: The average time it takes to respond to an issue, also known as mean-time-to-recovery.

    These metrics could be helpful indicators of a software development team’s overall agility and health. They can signal bottlenecks in the CI/CD process or highlight where too much time is spent remediating issues, encouraging positive changes. Solving these root causes could all improve a team’s productivity.

    Going Beyond DORA: SPACE

    DORA also added reliability to the list. That’s understandable, given the interest in building reliable systems. But things start to get a little more complex when SPACE enters the equation.

    Proposed in a 2021 paper by GitHub researchers, SPACE augments DORA with the following attributes:

    • Satisfaction and well-being
    • Performance
    • Activity
    • Communication and collaboration
    • Efficiency and flow

    Metrics like satisfaction or communication and collaboration are pretty subjective. Plus, it can be challenging to gather this data and encourage engineers to speak honestly in surveys. Measuring the activity of individual members, like the number of issued closed or pull requests made, isn’t exactly tied to end value and could even produce a negative psychological effect.

    “Consequently, teams can become fixated on them, as seemingly positive changes are relatively easy to achieve,” wrote James Walker. He adds that SPACE still doesn’t tell you why measurements have value—furthermore, it lacks actionable information and might be tricky to implement, given developer pushback against performance analysis systems.

    Opportunity-Focused Metrics

    The McKinsey report adds to the mix what it calls “opportunity-focused productivity metrics,” which include inner/outer loop time spent, a Developer Velocity Index benchmark, contribution analysis and a talent capability score. (It’s not totally clear how these metrics can be computed). It then organizes all these metrics into a framework that further segments measurements into system, team and individual levels.

    McKinsey's framework for measuring developer productivity

    While some believe in the framework’s legitimacy, many commentators don’t see the practicality of implementing such a framework in practice. “Introducing the kind of framework that McKinsey is proposing is wrong-headed and certain to backfire,” wrote the PragmaticEngineer.com. “The report is so absurd and naive that it makes no sense to critique it in detail,” wrote Ken Beck.

    Things to Avoid With Developer Productivity Metrics

    Apparently, developers are very weary of being measured. And given the recent backlash, it seems like overreliance on productivity metrics could negatively impact the engineering culture. So, engineering leadership should be careful when implementing these sorts of tactics. Here are some anti-patterns to consider.

    Don’t make it about individual performances. Metrics like DORA have become standards for measuring the efficiency of software teams, but they shouldn’t be applied at the individual level. “Measuring software development in terms of individual developer productivity is a terrible idea,” said Dave Farley. Technology development is more of a collaborative process; performance metrics should reflect how the team operates.

    Don’t measure developer productivity in isolation. On that note, performance metrics shouldn’t be judged in isolation. Issues with failures, for example, often depend on contexts outside of an individual developer’s control. At the same time, it can be hard to correlate individual developer contributions to end customer satisfaction.

    Don’t create an opportunity to gamify productivity systems. Leadership shouldn’t directly tie a developer’s salary, reputation or position to whether or not they hit certain metrics. If productivity systems carry this much weight, they could become gamefied.

    Don’t put in more effort than it’s worth. In tech, where there’s hype, there’s money to be made—and a host of vendors ready to hawk monitoring tools. What’s more, to enact these productivity measurement enhancements, McKinsey noted that “most companies’ tech stacks will require potentially extensive reconfiguration.” Devoting many engineering resources to monitor engineers just seems like a big pill to swallow.

    Don’t put too much faith in metrics. Lastly, metrics only show one side of the picture. They are only data points and can’t always depict what it’s really like on the ground. Furthermore, qualitative responses also might be skewed due to reluctance to speak up.

    Remember Goodhart’s Law and Focus On Quality

    Personally, I believe that measuring things like developer productivity, efficiency, experience and satisfaction does have some merit for some businesses. Most teams are now working asynchronously, and metrics help leaders take the pulse of a team’s performance, even if they are all remote workers.

    It’s also nothing new. Leadership has been attempting to measure developer productivity for years. But, these metrics often lack context and come with many caveats to consider. Thus, they shouldn’t be given a huge seat at the table.

    I like this ‘law’ from British economist Charles Goodhart, which is colloquially summarized as “when a measure becomes a target, it ceases to be a good measure.” In other words, when you start working for the metrics, the engineering culture and overall code quality might decline. This matters, since a separate study found that increases in code quality tend to be followed by an increase in developer productivity.

    So, focus first on establishing code quality and, hopefully, productivity will flow.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    CareerBuilder.com

    The DarkStar Group
    Fairfax, Virginia
    AWS DevOps Engineer (Security Clearance)
    $250,000

    Indeed.com

    Dutch Bros. Coffee
    Grants Pass, Oregon
    Senior DevOps Engineer
    $139,473 to $161,688

    LinkedIn

    Claravine
    Remote
    DevOps Engineer
    $105,000 to $115,00

    SimplyHired.com

    Motorola Solutions
    New York, New York
    Infrastructure Operations Engineer
    $93,620 to $180,000

    Dice.com

    Scholastic Inc.
    New York, New York
    Associate DevOps Engineer
    $58,500 to $93,500

  • DevOps is Making Gains on Mainframe Platforms

    DevOps is Making Gains on Mainframe Platforms

    A survey of 800 executive and technical professionals conducted by BMC found close to two-thirds (62%) are employing DevOps workflows to build and deploy applications.

    In addition, the survey also finds that just under a third of respondents (32%) are applying artificial intelligence to IT operations (AIOps) across all platforms.

    John McKenny, senior vice president and general manager of intelligent Z optimization and transformation at BMC, said the survey shows that organizations continue to invest in mainframe applications alongside distributed computing environments.

    In fact, 61% of respondents noted that data volume on mainframes increased in the last year, with 56% reporting an increase in the number of databases running. Well over a third of respondents (35%) also reported increased interest in taking advantage of cloud technologies to handle, for example, storage and data protection.

    Overall, the survey found that 94% of respondents have a positive perception of the mainframe, with nearly two-thirds (63%) reporting increased mainframe investments over the past year.

    Compliance and security lead the list of priorities for mainframe organizations, with 61% of respondents citing these as top issues, followed by cost optimization (49%), enhancing automation (41%) and data recovery (40%).

    In general, organizations with mainframes are adopting modern application development tools such as VS Code to attract a younger generation of developers. It’s critical to continue to attract developers to mainframe platforms that continue to run the most mission-critical applications in the enterprise, said McKenny. Many of those applications are being modernized using DevOps tools and practices rather than being moved to another platform, he added.

    He added that the appetite for using generative artificial intelligence (AI) to write code for mainframe applications is less clear because of intellectual property concerns. Most organizations are not going to want to expose their mainframe code to a general-purpose large language model (LLM) that drives a public generative AI platform, but there will be a role for private LLMs that are trained using code that has been validated, noted McKenny.

    Regardless of how generative AI evolves, there’s still much work to be done to further the adoption of DevOps best practices in mainframe environments. Each organization will determine how stringently to apply those practices based on the frequency at which applications running on mainframes need to be updated. In most cases, organizations with mainframes will use a mix of waterfall and DevOps-based approaches to building and deploying applications for many years. The decision as to which approach to employ will, naturally, have as much to do with culture as it does the rate at which the application needs to be built and deployed to drive, for example, a digital business transformation initiative.

    The days when mainframes were managed in isolation from the rest of a distributed computing environment are slowly coming to an end. A mainframe is, arguably, just another node for deploying applications. The challenge and the opportunity now is getting the right DevOps tools in the hands of the IT team managing heterogeneous IT environments, including mainframes.

  • Why Enterprises Should Embrace Data-Driven Software Management

    Why Enterprises Should Embrace Data-Driven Software Management

    Many Fortune 500 companies have recently undergone extensive layoffs and now find themselves having to achieve more with fewer people—while grappling with how to integrate generative AI into their products. They must build while economizing, streamlining and increasing efficiency in every part of their company. Yet one of the most vital cogs of the machinery has struggled to evolve over the past 20 years.

    In fact, until now, managing high-tech engineering teams at the world’s biggest companies has not been all that high-tech.

    Back in the 1970s, the “waterfall” model was introduced: An inflexible process in which each phase of software development depended on the previous one’s completion. Then, around the 2000s, companies started moving towards “agile” models that tracked progress in two-week sprints.

    Now, a third era of software development management is emerging. It’s not waterfall or agile; it’s data-driven.

    Current management systems cannot give managers real-time, granular insights so they can make decisions on the spot and run projects in the most efficient way possible. However, data-driven models generate live data on how teams are progressing which has far-reaching effects on businesses’ productivity and future survival.

    The emergence of DORA, a framework for tracking development metrics makes this possible. For the first time, new tools enable us to capture the software development life cycle automatically, without any manual input from the engineer, leading to fewer roadblocks, a 20% faster cycle time and greater ROI.

    Despite this, only a handful of Fortune 500 companies have embraced data-driven metrics. Here are the reasons that shift should happen now.

    Real-time Insights are Game-Changers

    Until now, companies have been using inaccurate methods to track their software development teams. Project management reports on engineers’ output are reviewed every two weeks, monthly, or even quarterly. This means that impactful changes are made at a delayed point in time rather than when they can make the greatest difference to cycle time.

    In contrast, DORA measures specific real-time performance indicators: Deployment frequency (DF), lead time for changes (LT), mean time to recovery (MTTR) and change failure rate (CFR) (For more information, see here). These far more data-driven metrics measure efficiency and velocity at every key point of day-to-day software development, with feedback to management happening continually.

    Often, individual engineers won’t have enough capacity or oversight to inform managers of specific issues during a cycle. Using DORA metrics is like being able to detect that a team has an abnormally high BPM though they are in a rest phase. It’s a signal of illness; that more or different resources are needed. That then requires you to go in and inspect the micro factors that can be adapted.

    For any Fortune 500 company, this quick turnaround is vital. Any small issue in a product cycle can easily escalate and become a critical problem in a matter of days. Instead, using DORA can accelerate software development and, ultimately, time to market.

    DORA Gives you Internal Benchmarks

    But, how can you tell if something is wrong while using DORA? In the first few months of adoption, it’s likely that companies will compare their internal data to industry benchmarks. Unprecedented data has recently been published that offers general performance benchmarks for each DORA metric based on industry research.

    But the end game is to be able to compare your DORA metrics to your own internal benchmarks. With time, each company will develop its own personal baselines that better represent internal capabilities and limitations based on innumerable variables.

    The purpose of DORA benchmarks, however, is not to draw a fixed red line. Think of it more like a speed limit that ensures that people are working at a steady pace while allowing for flexibility.

    A clear sign that your team may be burnt out is a really low cycle time. At times it can be a technical bottleneck, but the beauty of DORA is that it can also point you towards well-being issues.

    DORA Needs to be Coupled With Granular Insights

    DORA is unparalleled when it comes to flagging managers that something is wrong. DORA metrics can flag that a team has a high cycle time – for example, an abnormally long deployment frequency suggests that engineers are working more slowly or that there may be a build-up of code that isn’t being uploaded to the repository quickly enough during the review process. At that point, you need to zoom in to get more information. There are multiple metrics that provide insights into the exact pain points in a team’s development, and you can develop these internally or via tools or engineering management software that provides more holistic oversight.

    In this particular case, you can use a granular metric called “time to merge from first review” to see how fast engineers incorporate feedback from their peers during code reviews. If it is taking a week for the first review to be carried out, integrated into the code and finally uploaded to the general platform, then perhaps there’s a bottleneck occurring during the review process.

    Perhaps the most important benefit of Engineering Leadership 3.0 is that it no longer has to be a guessing game. If they sense something’s wrong, they can check their DORA dashboard, receive confirmation of the problem, hone in with granular metrics, and take corrective actions immediately.

    Teams that adapt their behavior as a result of these data-driven metrics will add incredible ROI to the company. But these changes can’t happen overnight. It will start with getting buy-in from engineering managers and the executive team. For Fortune 500 companies, that often requires elaborating action plans, integrating external tools to operate the tracking process and training engineering managers (in our case, this takes up to five weeks). But once they’re a part of the business, they will become indispensable in running smoothly without stacking up issues.

  • There’s no One-Size-Fits-All Data Mobility Strategy

    There’s no One-Size-Fits-All Data Mobility Strategy

    Whether you’re a Fortune 500 enterprise or a fledgling startup, there’s one thing every business can expect to confront: Change. This is resoundingly true when it comes to data management. Even greenfield operations will, sooner rather than later, need to change how and where they leverage their data. After all, every business—including those launched with the most cutting-edge technology—is built on a series of choices and compromises. As technologies and business priorities change, so should your data mobility strategy.

    In a world of ever-changing variables, what’s the right data mobility strategy? There is no one-size-fits-all solution. It does, however, help to start with a solid foundation on which to build your strategy.

    With the right platform that allows for flexibility and change, it’s time to build the structural framework for your data mobility strategy. When creating the framework for your data mobility strategy, there are a few considerations to keep in mind, including your desired use of on-premise storage, your hybrid cloud strategy and “supercomputing” capabilities, and the storage area network (SAN) needed based on your organization’s needs. Let’s review what they might mean for you:

    Developing a Hybrid Cloud Strategy

    The reality is that the hybrid cloud is now achievable for most organizations. According to Flexera’s 2023 State of the Cloud Report, 72% of organizations have embraced a hybrid cloud strategy. Your data mobility strategy should reflect that.

    With a proper hybrid cloud environment, the user experience should be seamless regardless of where your data exists, whether that’s on-premise, in a private cloud, or in the public cloud. As the potential for more dynamic resource allocation grows, your data mobility strategy inherently becomes more critical. Data mobility, in effect, now equates to computing mobility.

    Leveraging the “Supercomputer” in the Cloud

    Data mobility becomes even more imperative as entities gain more opportunities to take advantage of “supercomputing” capabilities.

    The computing power that exists in the cloud now is tremendous, and thanks to modern dynamic cloud pricing, even smaller organizations can tap into it. According to one estimate, the cloud-based high-performance computing market should reach $55 billion by 2030, growing at a CAGR of 7% from 2023 to 2030.

    So for example, if you’re a FinTech startup, you could pay by the minute to leverage high-performance computing capabilities to test your trading algorithm. Previously, these powerful capabilities were only available to the biggest customers — think NASA — and came with astronomical costs.

    On-Premise Solutions Aren’t Going Away

    Even as cloud services and capabilities become more accessible and more valuable, on-premise operations aren’t going to disappear. Customers have all kinds of reasons for keeping data on-premise, whether it’s for compliance purposes, costs or the desire to leverage existing infrastructure. Certain workloads, meanwhile, simply don’t make as much sense on the cloud.

    The reality is that even the most compelling alternatives couldn’t eliminate on-premise operations, because business ecosystems and industries evolve organically. Think about it: Many businesses still use landlines, and workers still use desktop computers. Nearly half a century after marketers first coined the term the “paperless office,” it took a full-blown pandemic to push our collaboration tools to the point where we are finally, basically, paperless.

    Fiber Channel and iSCSI

    When it comes to data mobility, how does an organization decide whether to adopt fiber, iSCSI or both? As organizations manage workloads that require low latency and other networking capabilities, they’re going to be investing heavily in this area.

    Traditionally, a significant amount of data management is fiber-channel-based. With iSCSI, an organization doesn’t need to invest in separate physical infrastructure, such as switches or cables. Moreover, iSCSI has become more attractive to entities moving workloads to the cloud. A whole range of products, from Pure Storage, NetApp and even cloud platforms like Microsoft Azure, offer iSCSI-based storage in the cloud. Your data mobility strategy should consider how to move from fiber channel to iSCSI and back again.

    Change is the Only Constant

    Ultimately, no one can predict all of the major technological innovations that will impact their business down the road. To stay nimble, you need a data mobility strategy that you can execute with the team you have — whether their primary expertise is in storage, networking or elsewhere. This can be especially challenging in large organizations, with teams that have to meticulously plan their projects and stay accountable for their budgets. Now more than ever, it’s important to have a solid foundation on which to build a strategy that fits your needs.

  • Summit Highlights Open Source Software Security Progress

    Summit Highlights Open Source Software Security Progress

    More than a year after the Open Source Security Foundation (OpenSSF) summit kicked off an initiative to better secure open source software-based software supply chains, a lot of progress has been made—but much work remains to be done.

    The OpenSSF this week hosted a Secure Open Source Software (SOSS) Summit 2023 event in Washington, D.C., during which it made available a Secure Open Source Software Vision Brief 2023. The brief describes the various efforts being made to improve open source software security, including, for example, providing maintainers of open source software projects with free DevSecOps tools.

    This week, the Cybersecurity and Infrastructure Security Agency (CISA) also published an Open Source Software Security Roadmap that defines its role in providing more visibility into how open source is being used and the associated risks.

    However, it’s not clear how much maintainers of these projects are embracing DevSecOps workflows. Many projects are led by a handful of maintainers that often lack the time and skills required to ensure the components they employ have not, for example, been compromised by malware that might find its way into downstream applications.

    Sonatype CTO Brian Fox said the focus of the summit’s participants will be on education and securing public code repositories. While there is a lot of focus on maintainers, he added that there is not enough attention to all the organizations that have not updated open source components within a software build with known vulnerabilities. Many IT teams are reluctant to upgrade components for fear of breaking applications with complex dependencies, but the amount of code that is potentially insecure is massive. “There’s a huge mountain to climb,” he said.

    The challenge is determining which code to upgrade first in a way that will have the most impact in terms of improving application security, added Fox.

    Shawn Ahmed, chief product officer for CloudBees, also noted that organizations should evaluate the DevSecOps processes being applied by maintainers of open source software projects. CloudBees, for example, reviews the plugins provided by the open source community for the Jenkins continuous integration/continuous delivery (CI/CD) platform for vulnerabilities every month, he noted.

    It’s also critical for organizations that consume open source software to contribute code to improve the security of open source software, added Ahmed. Too many organizations are focused on open source software security without devoting resources that would help resolve the issue. he added. “We need to make sure open source software is not a vulnerability threat,” said Ahmed.

    Moran Ashkenazi, chief strategy officer (CSO) and vice president of security engineering for JFrog, said on the plus side, there is a lot more awareness of open source software security issues since the disclosure of a zero-day vulnerability in the Log4j shell tool widely used to manage logs in Java applications. But added that this issue is not going to be resolved overnight. “That was a wakeup call,” she said.

    Tapabrata Pal, vice president of architecture for Fidelity Investments, noted that the fundamental problem is that cybercriminals have become more adept at injecting malware into software supply chains, so no one knows whether their application environments might be compromised. “It’s a serious issue,” he said.

    It’s not known whether the open source software that many developers routinely incorporate into their applications might contain vulnerabilities that could be exploited, but the level of technical debt that has accrued over the last several decades is considerable. The issue is finding a way to address it without becoming overwhelmed by the enormity of the task.

  • CloudBees CEO: State of Software Development is a Disaster

    CloudBees CEO: State of Software Development is a Disaster

    CloudBees CEO Anuj Kapur told attendees at a DevOps World event today that with developers spending only 30% of their time writing code the current state of software development in enterprise IT organizations is a disaster.

    After more than 14 years of effort, the promise of DevOps—in terms of being able to accelerate the rate at which applications are being deployed—remains largely academic, said Kapur. In fact, the effort to shift more responsibility for application security further left toward developers has only increased the amount of cognitive load and reduced the amount of time available to write code, he noted.

    However, with the rise of generative artificial intelligence (AI), an inflection point that will dramatically increase the velocity at which applications are being built and deployed has arrived, said Kapur. The challenge will be achieving that goal without increasing the cognitive load on developers. That cognitive overload results in 70% of developers’ time not being productive within organizations that often hire thousands of developers, he noted.

    Despite all the DevOps issues that need to be addressed, AI advances promise improvement. The overall DevOps market is still relatively young, given the current level of adoption, said Kapur. “We continue to believe the market is early,” he said.

    Today, CloudBees took the wraps off the first major update to the open source Jenkins continuous integration/continuous delivery (CI/CD) platform to have been made in the past several years. At the same time, the company also unveiled a DevSecOps platform based on Kubernetes that is optimized for building and deploying cloud-native applications based on portable Tekton pipelines. That latter platform provides the foundation through which CloudBees will, in the months ahead, apply generative AI to software engineering to, for example, create unit tests on the fly and automate rollbacks.

    In addition, DevSecOps capabilities will be extended all the way out to the integrated development environments (IDE) to reduce the cognitive load of developers.

    The overall goal is to reduce the number of manual processes that create bottlenecks that make it challenging to manage DevOps at scale.

    Criticism of the level of developer productivity enabled by DevOps compared to other development approaches needs to be tempered, said Tapabrata Pal, vice president of architecture for Fidelity Investments, because it still represents a significant advance. There is still clearly too much toil, but the issues that impede the pace at which developers can effectively write code tend to be more cultural than technical, he added.

    Organizations are not inclined to automatically trust the code created by developers, so consequently, there is still a lot of friction in the DevOps process, noted Pal.

    In theory, advances in AI should reduce that friction, but it’s still early days in terms of the large language models (LLMs) that drive generative AI platforms and their ability to create reliable code, he added. That should improve as LLMs are specifically trained using high-quality code, but in the meantime, the pace at which substandard code might be generated could overwhelm DevOps processes until AI is applied there as well, said Pal.

    Thomas Haver, master software engineer for M&T Bank, added that while assisted AI technologies will have a major impact, it’s not reasonable to expect large organizations to absorb them overnight. Patience will be required to ensure advances are made in ways that are both secure and sustainable, he added. In some cases, organizations may opt to build their own LLMs to achieve that goal, noted Haver. “You should not expect to change on a dime,” he said. “It will take time to see the fruits of our labor.”

    While many DevOps challenges need to be resolved, the benefits continue to outweigh the cost and effort required to succeed. The hope is that, with the help of AI, the return on those investments will continue to accrue.

  • JFrog swampUP: Keynotes and What’s Next

    JFrog swampUP: Keynotes and What’s Next

    At the sold-out JFrog swampUP conference this morning, the event’s keynotes were headlined by JFrog CEO Shlomi Ben Haim and co-founder and CTO Yoav Landman. The theme was “next”—what’s next for DevOps and what’s next for developers.

    Of course, there are several answers, but cutting to the chase, it seems security is job one. Developers are squarely in the crosshairs of the bad guys, and protecting them and their code is top priority at JFrog. To do so requires shifting security even further left into the integrated development environment (IDE) itself. Landman demonstrated new functionality in the JFrog Xray scanning and remediation solution’s IDE and emphasized the difference between quality assurance and quality testing.

    CEO Ben Haim took a higher-level view, looking at the issues facing organizations as they seek to develop more, develop faster and do so more securely. He also reinforced the themes hit on by Landman. Overall, the message from both co-founders was that the amount of code being deployed continues to increase exponentially. The need to go faster while being more secure is greater than ever, and JFrog has adopted that as the company’s mission, Ben Haim and Landman said.

    Audience enthusiasm was high, and despite growing to almost 1,000 employees, many of whom have been on board for less than a year, the culture at JFrog remains a constant focus. Landman and Ben Haim reiterated that the company is committed to their customers and their community. 

    That community was well represented, with all three of the major cloud providers exhibiting in the expo area, as well as a good mix of other DevOps providers like CloudBees (whose own DevOps World Roadshow kicked off today). Additionally, some of the new channel partners, such as IT Methods, also exhibited.

    Beyond the keynote, other JFrog presenters in the morning keynote sessions included the leadership of the Artifactory team and senior leadership of the security team.

    JFrog leaders were not the only ones on the agenda at swampUP. A leading games company presented a demonstration of their use of JFrog for CI/CD pipelines. One of the big three cloud providers presented a session discussing whether AI will replace developers (spoiler alert: The answer is no). Additionally, frequent DevOps.com contributors Tracy Ragan and John Willis also presented throughout the day. Our own Will Willis will be recapping some of those highlights.

    All in all, it was a return to normalcy for swampUP. This event felt much like previous swampUPs that occurred before  the COVID-19 pandemic began. The event was sold out with over 400 attendees, which tracks with what we’ve seen at other in-person events recently.

    JFrog has grown and matured but continues to emphasize what has made the company successful. We’re excited to see lots of new features for developers and DevOps practitioners to explore.

  • ngrok Adds Load Balancing to Programmable Network Service

    ngrok Adds Load Balancing to Programmable Network Service

    ngrok today added a global server load balancing capability to its service for managing ingress via an application programming interface (API).

    Alan Shreve, ngrok CEO, said the ngrok Global Server Load Balancing (GSLB) service reduces latency and increases overall resiliency by automatically rerouting traffic across a global network of points-of-presence ngrok manages.

    The overall goal is to provide a higher level of abstraction for managing network operations (NetOps) that can be programmatically invoked. NetOps takes the place of what are essentially the equivalent of Assembly programming tools to provision networking equipment, he added. The long arc of computing is moving toward higher levels of abstraction to make IT capabilities more accessible, noted Shreve.

    That approach eliminates the need for IT teams to assign network operations teams to manage appliances in favor of a service that also provides a demilitarized zone (DMZ) via a service that accesses reverse proxies and firewalls, noted Shreve. The ngrok service ensures unauthorized requests are instantly blocked.

    The ngrok service provides a programmable approach to managing network services that makes it simpler for DevOps teams to invoke them. Instead of waiting for a network operations team to provision networking equipment, an API makes it possible to integrate the provisioning of networking services within the context of a larger DevOps workflow.

    One of the challenges that IT teams continue to encounter is that while virtual machines can be spun up in minutes, the provisioning of network services can require anywhere from minutes to days, depending on the age of the networking equipment. The ngrok service doesn’t eliminate the need for network professionals, but it does make it simpler for them to collaborate with application development and deployment teams, said Shreve.

    It’s not clear if NetOps teams will one day simply become an extension of a DevOps team, but as applications become more distributed, the need to minimize network latency is becoming more critical. A service like ngrok makes it simpler for DevOps teams to ensure the appropriate level of bandwidth is available so distributed application performance doesn’t slow to a crawl.

    Of course, the bigger challenge will be bridging the cultural divide that currently exists between many NetOps and DevOps teams. Both groups have their own distinct nomenclature for describing services that, in many cases, the other simply doesn’t comprehend. As distributed application development accelerates, each organization will need to determine how to demolish those silos so both teams can more easily achieve a common goal.

    In the meantime, IT leaders might to reconsider their strategy as networking services become increasingly programmable via APIs. Most modern networking equipment today is software-defined in the sense that network operations teams have access to graphical tools to provision networking equipment. However, as APIs become more accessible, the need for those graphical tools may not be nearly as high as it is today as networking becomes just another service to be managed as code.