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Category: IT as Code

Nobl9 Adds Tools to Make SLO Queries More Efficient

At its SLOconf event today, Nobl9 added a Query Tester tool that validates whether queries made against the data its service level objectives (SLO) management platform collects will deliver meaningful results. Nobl9’s SLO management platform collects data in real-time from data sources such as New Relic, Datadog and Dynatrace to help customers meet SLOs.

In addition, a Query Checker tool will now automatically put any new SLO into a testing state to automatically validate that the query against that SLO will run correctly.

The company has also improved the precision of its calculations to provide DevOps teams with more accurate, reliable and actionable insights into the performance of services and added a Metrics Health Notifier toolset that provides insights into anomalous data being collected from various sources as well as what data might be missing. Nobl9 has also added a Custom Query Delay tool that gives DevOps teams more control over when data is pulled along with access to data source logs.

Kit Merker, chief growth officer for Nobl9, said the overall goal is to improve the overall resiliency of its platform for managing SLOs.

Finally, Nobl9 also announced that its platform is now available for the Google Cloud Platform (GCP) in addition to existing support for Amazon Web Services (AWS). As part of that GCP support, Nobl9 made available an educational site that provides example of how DevOps team can take advantage of the large language models (LLMs) that Google is making to improve SLOs or generate an SLO that is automatically converted into a set of YAML files.

A survey of 314 IT professionals conducted by Dimensional Insight on behalf of Nobl9 found 69% have implemented SLOs, with more than three-quarters of those respondents crediting SLOs for preventing business disruptions. A total of 80% also noted their organization has an increased focus on system reliability, with just over a quarter (27%) saying SLOs saved their organizations a half million dollars or more. A full 95% said SLOs are also driving better business decisions.

However, 97% also reported it is difficult to manage SLOs because of a lack of reporting, lack of application and environment support and interoperability issues.

Nobl9 is trying to spur greater adoption of SLOs by making available an open source SLO specification that defines a common interface for constructing SLOs across a Git-based workflow. Nobl9’s platform based on that specification has now been integrated with 24 data sources, said Merker.

SLOs, of course, are not a new idea. They have been employed as a metric to track the performance of IT services for decades. However, as more microservices-based applications are built and deployed, it’s becoming more challenging to maintain SLOs across applications that have many more dependencies than legacy monolithic applications.

Ultimately, each IT team needs to provide some sort of objective benchmark that assesses their overall effectiveness at delivering application services. SLOs-as-code are intended to make it simpler to gather the metrics that confirm whether service levels are being achieved. Those SLOs are then tracked and integrated across any set of application services a DevOps team cares to track.

May 15, 2023
AWS Converts Cedar Policy-as-Code Tool to Open Source Project

At the Open Source Summit North America conference today, Amazon Web Services (AWS) announced it is making Cedar, a language for defining permissions as policies that includes automated reasoning to mathematically prove an IT environment is secure, available as an open source project.

In addition, AWS launched SnapChange, an open source fuzz testing tool that cybersecurity researchers can employ to discover vulnerabilities by replaying physical memory snapshots in a KVM virtual machine. Fuzz testing uncovers software security issues by monitoring how the system behaves while processing random data.

David Nalley, director of open source marketing for AWS, said both projects are part of an ongoing AWS effort to contribute intellectual property to the open source community that plays a pivotal role in the applications that organizations deploy on the AWS cloud.

AWS has been employing Cedar to provide IT organizations with the ability to write authorization policies as code that can be deployed anywhere. It is most widely used within the managed AWS Verified Permissions service that manages those processes on behalf of organizations that deploy workloads on its cloud.

Cedar differs from other policy-as-code approaches because it employs automated reasoning, a computer science discipline that is considered a subset of artificial intelligence (AI), to enable machines to prove theorems and checks. That latter capability is especially critical to enable organizations to mathematically prove that compliance mandates have been continuously met, noted Nalley.

Otherwise, IT organizations become wholly dependent on an assessment of intermittent audits conducted by humans that are likely to arrive at varying subjective conclusions, he noted.

The latest open source contributions from AWS come at a time when there is more scrutiny being applied to how open source software is developed and employed. The scrutiny increased in the wake of a series of vulnerability disclosures involving open source software, such as the Log4j monitoring software project.

The maintainers of many open source software projects often don’t have the resources or capabilities required to immediately patch a vulnerability every time it is discovered. That’s become especially problematic, because more research into open source software vulnerabilities is being conducted; it’s only a matter of time before a vulnerability is detected in software that was largely created by developers that have limited cybersecurity expertise. Enterprises that consume open source software need to pay careful attention to what dependencies they are creating when that software gets incorporated into an application environment. A patch for a zero-day vulnerability that is discovered might not be readily available.

Hopefully, consumers of open source software will become more involved in its development by contributing code or making available the financial resources that maintainers need to devote time and effort to creating a timely patch to a vulnerability. In the meantime, however, they should also be reviewing what open source software is included in applications running in production environments. Cybercriminals are increasingly looking to discover and exploit vulnerabilities in not just existing applications but not also in components they know are likely to be employed across a wide range of downstream applications.

As such, finding ways to apply more cybersecurity expertise to application development by embracing DevSecOps practices has become an imperative rather than a recommendation.

May 10, 2023
GitGuardian Survey Surfaces Secrets Management Challenges

A survey of 507 IT decision-makers in the U.S. and the United Kingdom published today found 75% of respondents said a secret leaked from at least one application, with 60% noting that the leak caused issues for either the company, employees or both.

Conducted by Sapio Research on behalf of GitGuardian, a provider of a platform for securing application secrets, the survey found less than half of respondents (48%) are confident in their ability to protect application secrets to a great extent.

The top source of risks when it comes to secrets management are source code and repositories (58%), followed by hard-coded secrets (47%). Just under a third (30%) of respondents to the GitGuardian survey admitted they share passwords and secrets when building applications as clear text within a messaging application.

Just over a quarter (27%) still relied on manual code reviews to prevent secrets leaks, the GitGuardian survey found.

On the plus side, 94% of the respondents said they were considering improving their secrets management practices in the coming 12-18 months, even though 93% said either “all” or “some” of their repositories were continuously scanned for hard-coded secrets.

Thomas Segura, a cybersecurity expert for GitGuardian, said as the number of applications developed continues to increase, secrets sprawl has become a bigger challenge for application security. The way secrets are managed during application development has become a bigger issue in the wake of a series of software supply chain breaches that led to malware being injected into applications.

The concern is cybercriminals that compromise software development environments will be able to retrieve secrets that they can later use to compromise an application after it has been deployed in a production environment. Cybercriminals are also now routinely scanning for secrets that may have been stored in text after an application has been deployed.

The challenge is that secrets have not been historically managed effectively, so many organizations are now looking to implement more rigorous processes. Another challenge is that, in many cases, it’s not apparent whether those processes will be implemented by a DevOps team familiar with how an application is constructed or an application security team with more cybersecurity expertise, noted Segura.

Regardless of the approach to remediating these issues, there is always going to be an issue because the humans who build applications are always going to make mistakes. The goal is to minimize the number of secrets that might be leaked by implementing DevSecOps practices that automate the review process, said Segura.

In theory, at least, advances in artificial intelligence (AI) should make it easier to automate more of the secrets review process. In the meantime, however, organizations need to review their existing processes in advance of pending legislation that looks to hold organizations more accountable for application security if it is determined that the organization did not take reasonable precautions.

It may be a while before such legislation becomes law, but one way or another, it’s clear that DevOps teams are soon going to be asked tougher questions about how secrets were managed every time an application security breach occurs.

May 9, 2023
Grafana Labs Reins in Cost of Storing Observability Data

Grafana Labs today made available an ability to customize what observability data is collected via its managed cloud service. This capability can help reduce storage costs in addition to enhancing the dashboard Grafana provides to make it simpler to track metrics usage.

Wayne Jin, vice president of product marketing for Grafana Labs, said both capabilities would make it easier for DevOps teams to track and ultimately reduce the cost of storing the time-series data collected via its cloud platform.

As more DevOps teams use an observability platform to continuously collect metrics like traces and log data, they are discovering that the cost of storing all that high-cardinality data can easily spin out of control.

Grafana Labs is now making available an Adaptive Metrics capability that enables DevOps teams to fine-tune what data is collected and stored. However, in the event of an issue, DevOps teams can also turn back on all data the platform can collect to aid an investigation of the root cause of disruption, noted Jin.

The Cardinality Management dashboards Grafana Labs made available last year for paid tiers of its cloud service are now being made available to users of the free tier of service as well, Jin said. This will further help reduce storage costs by making it easier to identify metrics that are being collected but not used by anyone on the DevOps team, he added.

The Adaptive Metrics aggregation engine can then be applied to transform these metrics at ingestion into lower cardinality versions. Unused or partially used labels are stripped from incoming metrics, reducing the total count of time-series data collected. Adaptive Metrics also recommends aggregations based on an organization’s historic usage patterns, and DevOps teams can choose which aggregation rules to apply. Dashboards, alerts and historic queries are guaranteed to continue to work as they did before aggregation, with no rewrites needed.

Based on results reported by early users, Grafana Labs reported that Grafana Cloud Adaptive Metrics could eliminate an estimated 20-50% of the time-series data collected with no perceived impact on observability.

The Grafana Cloud service relies on an instance of open source Grafana Mimir to store data collected in a format originally defined for the open source Prometheus monitoring tool. While Prometheus is widely employed in Kubernetes environments, it’s also starting to gain traction as a monitoring platform in legacy monolithic environments. The amount of data being collected via that platform over time is steadily increasing. The Grafana Labs approach makes it possible for DevOps teams to reduce that data whenever there is no usage that needs to be investigated, for example.

It’s still early days as far as adoption of observability is concerned, but in uncertain economic times, there’s generally a lot more sensitivity to the total cost of IT. Many organizations are especially focused on reducing the cost of storing data in the cloud to rein in monthly spending on cloud services. Regardless of the motivation, collecting and storing data that isn’t needed is never the best use of cloud storage resources that, from a cost perspective, are just as finite in the cloud.

May 9, 2023
Consider Performance, Growth & Budget When Buying Data Analytics
So, your business needs to invest in data analytics technology to improve efficiencies, competitive advantage or business outcomes. There are a ton of things to consider, but underlying each decision are three driving factors:
- Performance – The need to meet service level agreements.
- Growth – The need to accommodate success, as well as deal with inevitable industry changes.
- Budget – The need to do both of the above while keeping costs low enough to not erode the profit gain from analytics.
These three forces underpin nearly every technology selection decision, but what frequently isn’t noticed is how much these three drivers interact.

1. Concurrency and Growth

Test both for current levels of concurrency and the level you hope to achieve in the next few years as the organization grows.

When evaluating benchmarks between vendors, consider that benchmarks are designed to make that company’s software look good. For example, if they are good at concurrency, you will see various levels of concurrency represented in the benchmark. If the benchmark shows only one workload at a time, or maybe ten, you can safely assume that concurrency is probably not something that technology is good at. Similarly, if the benchmark is done at 10 TB scale, the vendor probably isn’t that good at high-scale analytics.

When bringing vendors in for a proof-of-concept, one mistake I’ve seen (more times than I can count) is testing the capabilities of the software with only one user at a time. People do not line up politely to use analytics one at a time.

In addition to current levels of concurrency, over time, hopefully, the organization will grow, and the number of both internal analytics users and external customers will increase. Testing for the number of users you have now may not uncover problems that will invariably reveal themselves when a lot of people hit the system at once. The last thing your organization needs is to turn away customers because they’re overloading your systems.

2. Performance & Price

Evaluate price/performance, not performance alone.

Everyone understands the importance of performance. Not one user has ever gone to a data architect or engineer and requested for their analytics to “please execute more slowly.” But performance isn’t simply how fast the analytical software can process queries, how fast the data pipelines can move data or how fast the response is between data input and reaction output. There’s also a monetary aspect of performance to bear in mind.

In a recent benchmark, three data management and analytics stacks were compared with 60 simulated concurrent users/workloads. The difference in queries per hour on 250 TB of data was minor, perhaps 16 queries per hour for the slowest and 23 queries per hour for the fastest.

(Mcknight Benchmark – SaaS Data Analytics Platform Comparison – vendor names removed)

It looks like performance is just not that big a consideration for this technology. But there is no such thing as an unlimited budget. Let’s look at how much it would cost to get this level of performance out of each stack.

(Mcknight Benchmark – SaaS Data Analytics Platform Comparison – vendor names removed)

Price-performance is a far more useful metric of value to the organization. How much the software can do is irrelevant if the budget limits how much the business can afford to do with it.

3. Software Efficiency

Consider how much efficiency and control you’re willing to trade for ease-of-use.

When you analyze data in the cloud, you’re not just paying for software, you’re also paying for hardware, a.k.a. cloud compute instances. Bundling the two costs in one bill makes things easier. But it also means that cloud providers make more money the more instances, or compute nodes, you use. You trade ease-of-use in billing for a higher price. And they no longer have a financial incentive to provide efficient software.

Similarly, to make a cloud-based analytics software scale up in performance and concurrency with zero effort on your part, the software has to use only defaults for all settings internally. It can scale only by throwing more compute at the problem; compute that you’re paying for every time you use it. You’re trading control of the software–tuning, scaling guidelines, shut down conditions, etc.–for ease-of-use, again for a higher price.

Throwing more compute at a problem isn’t always the best solution. Recently, I saw a POC where the customer implementation included 278 EC2 nodes on Amazon cloud. The organization didn’t initiate the POC because of inefficiency. They were having performance problems, even with that much compute power. A competing product was able to run on only nine EC2 nodes with the same workload and better performance. The company was paying 25 times as much for the compute hardware because it was convenient.

Be aware of the tradeoffs.

4. Affordable Scalability

Paying for only what you use can become impractical when you use more and more.

The volume of data that is being created, consumed and captured is growing exponentially. By 2025, it is estimated that nearly 30% of data being generated will be real-time. Instead of pulling data in batches, data will be pulled in continuously. Queries will be responded to in seconds, not hours, or in milliseconds, not seconds.

Going to the cloud can make tremendous sense when a company or a workload in that company is new. But studies show that as businesses scale and growth slows, the pressure the cloud puts on business margins eventually outweighs its benefits. This is why most enterprises today are considering repatriating some or all of their workloads off the cloud.

5. Flexibility for Change

Make certain your stack will continue to work even as conditions change, and you won’t have to rebuild from scratch to do the same thing in a different location.

When you’re building a long-term architectural strategy, you want to ensure you cover as many bases as possible because, the fact is, you really don’t know what the future holds. Ten years ago, maybe you had a Hadoop on-premises strategy or a data warehouse appliance. Today, you may have a cloud-focused strategy. Tomorrow, maybe you’ll move some or all of those workloads back on-premises due to performance, security, costs, regulations or other reasons.

This is why you want flexibility that doesn’t mandate a “cloud-only” or an “on-premises-only” or a “this-cloud-only” architecture and which doesn’t integrate with only the same vendors’ software. You want an architecture that can work equally well both on the cloud as well as on-premises, hybrid or multi-cloud, providing the flexibility of going where business needs are best met.

Containerization offers possibly the highest level of flexibility in deployment, but it can also add to deployment complexity, so again, be aware of what you’re trading. If the software you choose is platform-agnostic—not tied to any particular location, ecosystem or deployment model—then that will make future choices a lot more flexible.

Regardless of what analytics you choose, what you decide to build or what use cases you have to meet, striving for balance in performance, growth and budget will create a strong data architecture, now and for the future:
1. 1. The analytics architecture is high-performance: it should meet SLAs, get things done on time and meet or exceed expectations and requirements.
  2. The analytics architecture is resilient to growth and change; even if there is rapid growth or sudden or extreme changes, it’s future-ready.
  3. The analytics architecture should control costs and live within an allocated budget.
Keep these guidelines in mind when choosing an analytics provider. The decisions you make today will have direct ramifications on the growth, profitability and success of the business tomorrow.
May 9, 2023
ReversingLabs: Increased Focus on Software Supply Chain Security

A global survey of 300 global executives, technology and security professionals found software containing vulnerabilities (82%) followed by secrets leaked through source code (55%), malicious code (52%) and suspicious code (46%) posed a serious risk to the business.

Conducted by Dimensional Research on behalf of ReversingLabs, a provider of a platform for securing software supply chains, the survey found 87% of respondents detected significant risks in their software supply chain in the last year. Nearly three-quarters (74%) said legacy application security tools, including static application security testing (SAST) (54%), dynamic application security testing (DAST) (42%) and software composition analysis (SCA) (40%), are ineffective against threats to modern software supply chains.

A full 88% said software supply chain security is an enterprise-wide risk, but only 60% said their software supply chain defenses were up to the task. Nearly two-thirds (65%), however, acknowledged their organization’s software supply chain security program wasn’t as mature as it should be. As a result, 80% are currently focused on improving security for the software supply chain, with 96% noting a more comprehensive approach to software supply chain security that detects more than vulnerabilities is needed.

Overall, the survey found nearly all respondents (98%) recognized that software supply chain issues pose a significant business risk. Nearly 90% reported detecting security or other issues in their software supply chain in the last 12 months. Those issues stemmed from internally developed software (47%) as much as open source software (49%), followed by commercial software (30%), the survey finds.

Tyson Whitten, vice president of global marketing for ReversingLabs, said the survey results make it clear that software supply chain issues extend well beyond open source software dependencies. Use of open source software has expanded greatly over the last decade, but security flaws in software are pervasive, he noted.

In addition, many of these issues stemmed from software created by contractors and third-party companies, added Whitten. More than half of respondents worked for organizations that use contractors (67%) and third-party development companies (59%) and many organizations don’t have the tools required to review applications before they are deployed in a production environment.

Software supply chain security has become a major issue in the wake of a series of high-profile breaches. The challenge is shortage of cybersecurity expertise available to secure those supply chains. Usually, someone on the DevOps team needs to emerge as a security champion for the good of the larger application development team. Unfortunately, most developers have had limited exposure to cybersecurity training, so it can take a significant amount of time for those champions to emerge.

In the meantime, it’s less clear whether organizations will upgrade their DevOps environments to make them more secure, but it’s clear they will soon be held more accountable for application security. Governments around the world are currently drafting legislation that would impose penalties should it be determined an organization recklessly built and deployed software. Once that legislation is passed, it may only be a matter of time before the pressure on organizations to adopt DevSecOps workflows increases. The issue is determining how quickly to get in front of that issue before a potential legal quagmire ensues.

May 5, 2023
Survey: Much Greater Focus on Reining in IT Costs

A survey from Vega Cloud found 62% of respondents are more closely monitoring all IT costs, with more than half (52%) more closely evaluating their cloud service provider options.

The survey polled 100 vice presidents from organizations with more than 1,000 employees and was published today by Vega Cloud, a provider of a platform for managing multiple clouds.

Overall, nearly half of respondents (48%) said they expected to reduce their IT spending a little or a lot in 2023, with 65% identifying the reduction of cloud spending as a specific goal. Three quarters (75%) are looking to cut cloud costs by 10-50% in 2023, the survey found.

A full 93% reported they have invested at least a fair amount of money on tools to optimize cloud costs, with 82% having invested in FinOps staff or people with FinOps experience. The challenge is that 70% of respondents said it was difficult to find people with these skill sets, the survey found.

The best way for organizations to reduce cloud costs is to become more informed about what they’re spending (40%), and 33% said that the best approach is to consistently optimize use of cloud resources. A total of 61% identified reserved instances/savings plans as the best way to optimize cloud use, while 55% said optimization was best achieved through waste elimination followed closely by right-sizing (54%).

Vega Cloud CEO Kris Bliesner said it’s clear the days when organizations allowed developers to consume cloud resources at will are coming to an end as cloud services consume a much larger percentage of the IT budget. In fact, the survey noted that 91% of respondents are spending at least one million dollars annually on cloud infrastructure alone.

The issue is that finance departments not only want to reduce cloud spend but also want cloud spending to become more predictable from a budget perspective. Nearly half of survey respondents (45%) said they’d received a surprise in their cloud bills “a number of times” over the past 12 months, with 70% of respondents noting surprises have happened at least a half dozen times in the past year.

A total of 40% admitted they had exceeded budgets multiple times in the past year, and 38% said they had to modify their forecast cloud spending budget numerous times over the past 12 months. More than half of respondents (55%) said database costs are the metric that matters most when looking at cloud usage followed by bandwidth costs (51%), storage costs (49%) and compute costs (47%). Nearly two-thirds (64%) said they track metrics at least somewhat often, while 46% said they have somewhat optimized their cloud use. A total of 11% said they’re not optimized at all.

A full 88% said they’re using artificial intelligence (AI) at least somewhat to optimize cloud use and costs, with nearly three-quarters (74%) noting they think ChatGPT or a similar generative AI tool could be used to optimize cloud use/costs further. Specifically, 42% said they thought it could improve decision making and 30% said it could automate decision making.

Among the cloud service providers, Google is cited as doing the most to help customers manage and reduce cloud costs (38%), followed by Amazon Web Services (34%) and Microsoft (28%)—but a full 85% said they could all be doing more.

Of course, changing economic conditions are naturally driving organizations to look harder at cloud spending. Well over half of respondents (56%) said they are concerned about the economy, and 95% of those respondents said those concerns have, at least somewhat, impacted their IT strategy.

It’s not clear whether organizations will continue to exercise greater control over cloud spending should the economy recover. For now, at least, there’s clearly more desire for control than ever.

May 4, 2023
FIDO/WebAuthn Passkeys is Inevitable: Get on the Train ¦ IBM CEO Hates WFH

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: The Passkeys authentication standard gets a huge boost, and IBM’s Arvind Krishna wants workers back in the office. (more…)

May 4, 2023
How DevSecOps Addresses Supply Chain Security

“The absence of security in the initial stages of system engineering is the single most significant cybersecurity gap and risk in modern system development.” This quote from tech entrepreneur Linda Rawson is a good reminder for the current cybersecurity threat situation. With software supply chain attacks increasing in aggressiveness and sophistication, organizations need to understand that cybersecurity should no longer be an additional process, let alone an afterthought.

Integrating security in the software development process provides a few other benefits, including better efficiency and customer satisfaction. However, its most notable benefit is addressing cybersecurity threats more effectively compared to the conventional ways of enabling software security.

The Rise of DevSecOps

DevSecOps is a fast-rising star in today’s search for effective cybersecurity solutions. The DevSecOps market is expected to be worth $23.63 billion in 2030, growing at a CAGR of 23.84% in the forecast period 2023-2030. Its value in 2022 was estimated at around $4.27 billion.

This double-digit growth is actually a bit conservative. Other forecasts showed higher CAGRs in the 30% range. This growth hints at the strong demand for DevSecOps solutions. Many organizations have started to acknowledge the benefits of implementing security validation methods early in the software development life cycle (SDLC) instead of doing them in bulk as a separate phase at the end of the development process.

Many organizations are now convinced that they can benefit from shifting left or bringing security into the SDLC earlier. Tools and solutions like static code analysis (SCA), automated dynamic analysis, interactive application security testing (IAST) and source composition analysis address security weaknesses before an application is released to the market. This allows organizations to release apps faster and, because of the elimination of most bugs and vulnerabilities, provide considerably better user experiences.

Vulnerabilities will never be completely eliminated, but they can be significantly reduced and more properly resolved while software is being developed if security validation is integrated in the development process. Some software defects and weaknesses tend to be more difficult to address through a separate security testing process, since it would entail a broader code tracing or review as opposed to quickly spotting the affected code when security validation is done alongside the development process.

Addressing Software Supply Chain Attacks

So, how does DevSecOps help prevent supply chain attacks? The key is in the visibility and control DevSecOps affords.

DevSecOps requires vigilance of security issues throughout the software development process. Security is not relegated to a different team that doesn’t understand the specifics of a development project. As such, the team has broad visibility over security concerns. Because the DevSecOps team also has mastery of the code being developed, it is easy to trace the origin of vulnerabilities and implement the necessary corrections.

DevSecOps is aided by automated tools that scan code for security issues and detect threats even before they manifest themselves as problems. These tools are used across all phases of the development process, from the time code bears basic features and functions to deployment and post-production.

Static testing: Even before an application’s code is ready to run, it can be subjected to static testing to find vulnerabilities. Tools such as static application security testing (SAST) can analyze code and detect possible security problems.

How does this address software supply chain attacks? Static testing can be overlaid on automated CI/CD pipelines to stop code that has security weaknesses from getting committed to the codebase.

Dynamic testing: An automated dynamic analysis can be used for code that is already executable. This is done through dynamic application security testing (DAST) tools, which are capable of detecting vulnerabilities usually invisible to SAST but which are detectable once the code is running.

How does this address software supply chain attacks? Organizations can implement automated black-box testing for apps in the CI/CD pipeline(s) to detect security flaws for apps that are already executable. This addresses the vulnerabilities that have not been detected by static testing while reducing the costs associated with the corresponding remediation.

Interactive app testing: This is a combination of static and dynamic testing. It employs IAST tools which run static testing on available code and come up with bespoke dynamic tests for a specific app to identify issues more thoroughly.

How does this address software supply chain attacks? Pure static and dynamic testing usually apply to earlier stages of the development process, whereas interactive testing addresses vulnerabilities that emerge in the later parts. By bringing IAST to the CI/CD pipeline, organizations achieve better security issue detection outcomes, which means the prevention of malicious code deployment.

Supply chain analysis: This entails the use of tools that specifically examine the security of third-party libraries and dependencies. To be clear, this is not the only method for addressing software supply chain vulnerabilities. In this case, supply chain merely refers to the external libraries, dependencies and other components that do not originate from the development team.

How does this address software supply chain attacks? Integrating software chain analysis tools in the CI/CD pipeline significantly reduces the adverse effects of vulnerabilities in dependencies and other components on a project’s codebase as well as on the development process itself.

Security-as-Code

One of the best practices developed in line with the integration of security in the software development process is the idea of implementing security-as-code. This means that security policies and measures such as testing and validation are turned into code whenever possible. In other words, the code itself already bears security mechanisms. Security testing runs automatically whenever code is committed. This ensures robust, consistent, highly scalable security that does not rely on external rules and mechanisms to prevent the introduction of anomalous code or malicious inputs.

Security-as-code also means that the DevSecOps team can see how modifications to code and the underlying infrastructure are created. The team can map out the impact of code changes and identify the areas where security tests and regulations can be implemented to optimize processes and avoid unnecessary delays.

Security-as-code is essentially the automation of security practices. It is not applicable in all instances, though, so it is still important to be knowledgeable about security testing methods that can be used in different stages of the development process.

Securing the Software Supply Chain

The SUNBURST incident and other similar high-profile attacks should serve as a warning for all organizations to ascertain the security of their software supply chain. DevSecOps is one of the best solutions right now. It does not guarantee the complete elimination of all threats, but it ensures that if threat actors succeed in getting around security controls, a breach won’t go unnoticed.

May 3, 2023
How Good Developers Become Good Engineering Managers

Being a good engineering manager means that, to some degree, you have a natural affinity for the work involved. And if you’re a good developer, you likely already have some of those innate skills and attributes.

But simply having the skills doesn’t mean you’ll enjoy being an engineering manager. For example, the nature of the role is that you’re no longer the one writing the code. I’ve known great architects who became great managers, but they ended up going back to being individual contributors (ICs) because they missed working with nitty-gritty coding details and the joy that brought them.

It helps to take a realistic inventory of your skillset and preferred working style before making the transition. There are certain competencies you should have completely dialed in, and other tips to know that will help your transition.

Basic Skills of a Good Engineering Manager

If you’re a good engineer, you likely already have the following foundational skills that also make a good engineering manager. With these in your toolbox, you’ll have a better chance at success.

1. Attention to detail. The buck stops with the engineering manager. You need to know when to push and ask questions and how to set outcomes for your team’s accountability. If you work loosely as an IC and need reminders about your own work, you probably won’t do well as a manager.
2. Amazing personal planning skills. As an engineering manager, you’ll be planning an entire team’s work. If you’re terrible at planning your own work, maybe the engineering manager role isn’t for you.
3. Speak intelligently about broad technical topics. If you’re a good developer, this shouldn’t be a concern. You’ll need this knowledge to advocate for your team and justify their work to upper management.
4. Team player attitude. Even as an IC, you should have a collaborative, team-oriented mindset. It goes without saying that you’ll need this when leading others.
5. Interest in managing others. This might seem obvious, but if you don’t have a natural interest in managing others and in the people side of engineering, you’ll be unhappy as an engineering manager.

Making the Transition From Developer to Engineering Manager

If you can do all of the above and are ready to make the transition from developer to engineering manager, here’s what to be aware of as you navigate that process. These tips aren’t meant to deter or discourage you but rather provide a helpful dose of reality.

Satisfaction in your work takes new forms. If you’re an excellent developer, you know the value you bring and likely get personal satisfaction out of the contributions you make. As a manager, it’s harder to see the direct value you provide. Shift your mindset and realize that now, you’re the one enabling developers to do that rewarding work, and that’s the value you provide. You’ll learn to take enjoyment and fulfillment from your team’s accomplishments.

People take up a lot of your time. What most frequently catches new engineering managers off guard are the people-related aspects of the role. They’re not prepared for how much time managing people takes. I’ve seen it over and over again where a new manager thinks they can still set aside time to code, but you really have to prioritize learning the craft of management.

Remember to communicate upward. In the absence of information, it’s human nature to assume the worst. When I was at BitBucket, I assumed that because I had always done a good job, the leaders would think my team was crushing it. The opposite was true; leadership thought everything was on fire.

I started sending updates every two weeks to various levels of management, and that’s when I learned the power of upward communication. I realized I needed to sell my team’s work, contributions and challenges. Learn what higher-level leaders are looking for and then give them that information regularly.

Context-switching is part of the job. As an IC, constant context switches kill your day. But being an engineering manager means dealing with endless context-switching. Going to multiple meetings is baked into the job, and you’ll still have to find ways to block off time to get other work done.

Shield your team. You also need to be prepared to help protect your team from context-switching. You might learn information that’s important to your team during your many meetings, but not all of that needs to be communicated with them. Filter out the junk so it doesn’t rain down on them and they can maintain their flow.

Being an engineering manager can be a rewarding role that has a considerable impact on an organization. The more prepared you are for the realities of the role and its differences from being a developer, the more you’ll enjoy it and the more successful you’ll be.

May 1, 2023