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Attaching Evidence to Alerts: an Enrichment Sidecar for Alertmanager

Every on-call engineer knows the ritual. A page lands in Slack: the 5xx rate on a service is above the threshold. The message tells you that something is wrong and nothing about what. So you open Kibana or Loki in another tab, set the time window to the last few minutes, filter by the service, and start reading. In my team this took two or three minutes on every alert, and these are the worst minutes of the incident, because nothing is being diagnosed yet.

The obvious request is to put the last few error lines into the alert message itself. I tried to do this inside Alertmanager, and it cannot be done there. It helps to understand why before building anything around it.

Why Alertmanager Cannot Do It

Alertmanager renders notifications through Go templates, and a template can only use what is already attached to the alert: the labels and the annotations which Prometheus put there when the rule fired. There is no mechanism which would call Elasticsearch, Loki or any other HTTP API at the moment a notification is sent. Annotations are rendered from the alerting rule at evaluation time, inside Prometheus, and Prometheus does not talk to log stores either. Fetching data at send time is simply not a capability of the design.

So the logs have to come from a separate process which stands next to Alertmanager.

The Sidecar

What runs in production now is a small service next to Alertmanager, and its shape came out of two failed attempts, so I will describe it through them.

My first attempt was a bot which received alerts and posted them to Slack itself, with the logs already attached. It looked good in a demo, but it was a mistake because the bot had become the paging path. Any bug in my code, any timeout of the log store, and nobody gets paged. I threw that version away quickly. Alertmanager has to stay the thing which posts the alert, exactly as before, and whatever adds context has to be allowed to die without anyone noticing. So the enricher hangs off a second receiver: the route which sends the alert to Slack also duplicates it to a webhook, and behind the webhook stands my service. When it is down, the page arrives as it always did, just without the extra.

The second attempt posted the logs as its own message into the channel, right after the alert. Within a day the channel was hard to read: two messages per alert, and during an alert storm they interleaved, so the logs stood three messages away from their page. The fix was to reply into the thread of the message which Alertmanager had already posted. The channel stays as it was, one message per alert, and whoever opens the incident an hour later finds the logs from the exact minute of the page sitting under it.

Finding the Message to Reply To

That thread reply is the one interesting engineering problem in the whole service. The enricher receives the alert from Alertmanager, and it has to find the Slack message which corresponds to this alert, and nobody tells it which one that is. When the webhook fires, the service reads the last couple of minutes of channel history and looks for a message which carries the same alert fingerprint. Our Slack template rendered the fingerprint into the message, so the match was exact; before I added that, I matched on the label set which appears in the message text, and that worked too. Once the message is found, the reply is one API call with thread_ts.

Then production corrected the details. Alertmanager re-sends a firing alert on repeat_interval, and my version at first posted the same log dump under the same message again and again, so now the service remembers which fingerprints it has already enriched. The lookback for matching is limited to about two minutes, since replying to some old message which happens to carry the same labels is worse than staying silent. The query into the log store is built from the labels of the alert (service, namespace, severity) and takes the last few error-level lines, ten or so. I keep it that short on purpose: the reply is a hint for the person, and the full picture is in the log store anyway. One more thing I did not expect: an empty result is also worth posting. When the reply says there were no matching error logs in the last five minutes, the person on call can broaden the investigation to the infrastructure or the alerting rule itself, and this saves the same minutes.

What It Changed

The service is a few hundred lines, and in operation it has been boring, and boring is what I want from anything standing near paging. The visible change for the team: when you open Slack on a page, the first evidence is already under it, and the phase of “which dashboard, which index, which time window” is gone from incidents. Time to diagnosis dropped on every incident which had logs behind it, without any change to the services themselves.

Logs turned out to be only the first thing worth attaching. The same service can reply with anything which Alertmanager cannot know at render time: a runbook link picked by label, the marker of the latest deploy of the affected service (the first question in half of the incidents anyway), or a dashboard link already scoped to the incident window. Alertmanager alerts, and the sidecar carries what the responder reaches for in the first minute.

If you run Prometheus with Slack, the whole thing is one small service plus one extra webhook receiver in the Alertmanager config. Do the idempotency bookkeeping first; it is the first thing which bites.

Daniil Romashov is a Site Reliability / DevOps engineer working on reliability and observability for high-load systems. Open-source work: github.com/youngpabl0.

October 8, 2026
New Relic Debuts Command-Line Observability Tool for Developers and AI Agents

New Relic has unveiled Ground Truth CLI, a command-line tool that enables software developers and AI agents to analyze system performance and verify whether technical issues have been resolved without needing to use dashboards.

The release reflects a big shift in enterprise IT as AI agents take on more advanced troubleshooting tasks. Traditional observability dashboards are designed for human users, while AI agents need direct, programmatic access to production data to investigate problems and evaluate potential fixes. Ground Truth CLI allows this access through the command line, enabling agents to work with operational data without needing a graphical interface, an approach known as headless observability.

In essence, Ground Truth CLI supports a development environment in which software engineers and AI agents work together to investigate failures, verify fixes and maintain production systems.

Of course, this shared responsibility raises questions about how much responsibility AI agents can assume in production environments. Giving agents access to system data supports more sophisticated troubleshooting, so in many cases engineers still need to evaluate findings and determine whether proposed changes are appropriate.

Currently in limited preview, the tool connects to New Relic Autopilot, NerdGraph and memory APIs, providing access to system telemetry and AI-powered analysis.

Automated Recovery Checks

The CLI also supports automated recovery checks, an important capability for IT teams managing applications that must remain available around the clock. For instance, an engineer troubleshooting errors in an online checkout application could use the tool to examine application behavior and identify possible causes. Following a software update, the engineer could test whether error rates have returned to acceptable levels.

These checks can be repeated automatically, providing evidence about whether the application is operating within specified recovery conditions. Of course, successfully deploying a software fix doesn’t necessarily mean the underlying problem has been resolved. Applications may continue experiencing intermittent failures even after an initial test indicates that a repair was successful.

Ground Truth CLI also records investigation results, enabling engineers to examine earlier findings or transfer an unresolved incident to another team member. The capability could be particularly useful for enterprise IT departments where troubleshooting often involves multiple engineers working across different shifts or development teams.

Another major component is integration with New Relic Autopilot, which uses specialized AI agents to assist with site reliability engineering tasks. Through the CLI, AI coding tools can access operational information and New Relic capabilities programmatically. The tool also supports scripts and continuous integration pipelines, potentially enabling production data to support automated software development processes.

New Relic already offers Ground Truth connectivity through the Model Context Protocol (MCP), which allows compatible AI tools to access operational information. The new CLI provides another method of accessing those capabilities, now designed for terminal-based workflows.

October 8, 2026
From Software Supply Chains to AI Vulnerabilities: Why Neither Solves Enterprise Linux Security

For nearly a decade, cybersecurity has been dominated by one overarching concern: securing the software supply chain. Organizations invested heavily in Software Bills of Materials (SBOMs), artifact signing, provenance frameworks, reproducible builds, and vulnerability scanners capable of identifying compromised dependencies before software reached production.

The software supply chain became the industry’s focal point, accelerated by incidents such as SolarWinds, Log4Shell, XZ Utils, and the increasing sophistication of nation-state attacks targeting open source ecosystems.

Today, however, the spotlight has shifted once again. AI models, autonomous agents, prompt injection attacks, model poisoning, insecure MCP servers, and malicious agent interactions have become the new security frontier. Vendors are rapidly introducing AI security platforms capable of monitoring prompts, identifying unsafe agent behavior, validating tool usage, and detecting model vulnerabilities.

This transition is understandable. AI introduces entirely new attack surfaces that did not exist just a few years ago.

Yet the industry’s attention may once again be focused on only one layer of the problem.

Neither software supply chain security nor AI vulnerability detection addresses the operational reality inside most enterprise datacenters: organizations continue to run operating systems containing thousands of known vulnerabilities because they simply cannot upgrade them.

That is where the real security challenge remains.

The Vulnerability That Everyone Already Knows About

Security teams have become exceptionally good at finding vulnerabilities.

Modern scanners can enumerate every installed RPM or DEB package, compare versions against multiple CVE databases, prioritize findings using EPSS scores, calculate exploitability, and even estimate business risk.

Finding vulnerabilities is no longer difficult. Fixing them is.

A typical enterprise Linux server may contain several hundred packages, many of which are deeply intertwined with application stacks built over many years. Updating a package is rarely an isolated action. Library upgrades frequently introduce new dependencies, remove deprecated APIs, alter runtime behavior, or require kernel updates.

Package managers such as DNF, YUM, APT, or Zypper make upgrades technically straightforward. Keeping production applications operational afterwards is another matter entirely.

Why “Just Patch It” Doesn’t Work

Security guidance often assumes that vulnerabilities should simply be patched immediately. In practice, enterprise infrastructure operates under different constraints.

Mission-critical systems frequently require months of qualification before new package versions are approved. Financial institutions, healthcare providers, manufacturers, telecommunications companies, and governments often certify complete software stacks rather than individual packages.

Updating OpenSSL, glibc, Python, Java runtimes, PostgreSQL, or even seemingly minor shared libraries may invalidate those certifications.

Application owners therefore postpone upgrades until maintenance windows become available. Sometimes those windows occur quarterly. Sometimes annually.

In some industrial environments, downtime itself is measured in millions of dollars per hour. Every upgrade introduces operational risk. Every postponed upgrade introduces security risk. Organizations continuously balance these competing priorities.

Uptime Has Become a Security Requirement

Historically, uptime belonged to operations while vulnerability management belonged to security. But now those disciplines are inseparable.

Modern enterprises increasingly operate around-the-clock services spanning multiple continents. Even rolling upgrades across Kubernetes clusters, virtual machines, and bare-metal infrastructure require orchestration, testing, rollback planning, and extensive validation.

The operational cost of maintaining continuously patched environments has become enormous.

Kernel updates frequently require reboots.

Library upgrades may require restarting hundreds of dependent services.

Container images must be rebuilt, retested, and redistributed.

Infrastructure-as-Code pipelines need regeneration.

CI/CD workflows must execute full regression suites.

Ironically, security improvements themselves can reduce overall system availability.

Backward Compatibility is the Hidden Cost

The largest obstacle is not installing updated packages. It is preserving application compatibility afterwards.

Enterprise software often depends upon behavior that was never formally documented but nevertheless remained stable for years.

A newer version of OpenSSL may remove legacy cipher suites. A Python interpreter upgrade may deprecate modules still used by internal automation. glibc updates occasionally change subtle runtime behavior. Kernel changes affect drivers, networking modules, storage systems, and proprietary software. Legacy applications frequently require code changes simply to continue compiling.

Modernizing applications therefore becomes a prerequisite for patching infrastructure. Security suddenly becomes a software engineering project.

Many organizations lack the engineering resources necessary to continuously refactor decades of accumulated applications merely to remain current with operating system releases.

AI Doesn’t Eliminate This Problem

AI-powered vulnerability detection undoubtedly improves visibility.

Large language models can explain CVEs, prioritize remediation, generate patches, recommend mitigations, and even assist developers in refactoring incompatible code.

Agentic systems increasingly automate large portions of vulnerability management workflows.

These are meaningful advances. However, AI still assumes that software can eventually be upgraded.

If production constraints prevent operating system updates for six months, AI does not eliminate that limitation. Knowing more quickly that a vulnerable package exists does not reduce exposure if the package cannot be replaced without risking business continuity. The bottleneck remains operational rather than informational.

Runtime Protection Changes the Equation

This is why the next evolution in Linux security is likely to move away from purely reactive patch management toward runtime protection.

Rather than assuming vulnerabilities can always be removed immediately, organizations increasingly need mechanisms that reduce exploitability while vulnerable software continues operating.

Runtime behavioral monitoring, application allow-listing, kernel integrity verification, immutable infrastructure, memory protection technologies, eBPF-based telemetry, system call enforcement, privilege minimization, and autonomous anomaly detection all contribute to reducing the attack surface without requiring immediate package replacement.

These approaches acknowledge an uncomfortable reality — vulnerabilities often remain present for extended periods.

The objective therefore shifts from eliminating every CVE immediately to preventing successful exploitation while business operations continue uninterrupted.

Security Must Adapt to Operational Reality

The cybersecurity industry has repeatedly shifted its attention from perimeter defenses to endpoint detection, from endpoint detection to software supply chains, and now toward AI security.

Each evolution has solved important problems.

None has removed the operational realities faced by enterprise infrastructure teams. Linux servers running critical workloads cannot always be upgraded tomorrow because of their impact on business operations. Some support manufacturing lines. Others control hospital systems. Some execute financial transactions worth billions of dollars daily.

For these environments, security cannot depend exclusively on maintaining perfectly current package versions. Instead, it must assume that known vulnerabilities will coexist with production systems for longer than anyone would like.

The future of enterprise Linux security will therefore require more than better scanners, richer SBOMs, stronger provenance, or AI-powered vulnerability analysis. Those technologies provide increasingly accurate diagnosis, but diagnosis alone is not treatment.

The organizations that will manage cyber risk most effectively over the coming decade will be those that combine software supply chain integrity, AI-assisted vulnerability intelligence, and continuous runtime protection into a unified security architecture. Rather than assuming every vulnerability can be patched immediately, they will focus on minimizing exploitability while preserving application stability and business continuity.

That is ultimately the challenge security has always sought to solve — not simply identifying vulnerabilities, but enabling organizations to operate safely in spite of them.

October 1, 2026
A Semicolon in a Branch Name Was All It Took to Steal an AI Agent’s GitHub Token

AI coding agents don’t just suggest code anymore. Tools like OpenAI’s Codex spin up a real container, clone a real repository, and authenticate with a real GitHub credential to get the job done — which means every agent your team wires up is also a new privileged identity, holding real access, running with comparatively little of the scrutiny a human with that same access would get.

A Branch Name Was the Whole Attack

In March, BeyondTrust’s Phantom Labs disclosed a critical command injection vulnerability in Codex. The flaw was almost absurdly simple: when Codex creates a task container, it passes the target branch name into a shell command without sanitizing it first. Characters like ; && | $() and backticks get interpreted literally by Bash.

The proof-of-concept needed nothing more exotic than that. Set the branch to main, append a semicolon to terminate the intended git command, then inject a second command that writes the output of git remote get-url origin — which contains the GitHub OAuth token in cleartext — to a file. Then simply ask the agent, in the prompt, to read that file back. Codex did exactly what it was built to do: it read the file and returned its contents. The stolen token came back inside the agent’s own task output.

The flaw hit every surface Codex ships — the ChatGPT web interface, the CLI, the SDK, the IDE extension — and researchers confirmed it could be automated to compromise multiple users sharing a repository, not just one. Fixing it took OpenAI roughly six weeks of iterative hardening before the issue was classified Critical and cleared for public disclosure.

The Bug Isn’t the Real Problem. The Blast Radius Is.

A sanitization bug gets patched. What doesn’t automatically get fixed alongside it is how much a compromised agent credential is actually worth — and on that question, Teleport’s 2026 State of AI in Enterprise Infrastructure Security report, based on interviews with 205 CISOs and security architects, has numbers worth sitting with: organizations that over-provision AI systems see 4.5 times more security incidents than those enforcing least privilege. 70% admit they grant AI agents higher access than a human doing the identical task would get. 67% still rely on static credentials for AI systems.

The Codex flaw illustrates the related problem of permission scope: a single unsanitized string field fed a container holding a GitHub token with access extending beyond the immediate task. The vulnerability made the theft possible. The permission scope decided what the theft was actually worth. Stealing a token that can only touch one branch of one repo is an inconvenience. Stealing one with broad organizational access can become an organization-wide GitHub compromise that happens to have started inside a coding assistant.

Gravitee’s 2026 State of AI Agent Security report found a similar confidence gap: 82% of executive respondents said they were confident their policies could protect against misuse or unauthorized agent actions, even though, on average, only 47.1% of an organization’s AI agents were actively monitored or secured. That gap is exactly where an unsanitized branch name turns into a production incident nobody saw coming.

What to Actually Check Before You Ship an Agent

● Scope the credential to the task, not to the developer. If an agent only needs to open a pull request on one repository, it has no business holding a token with the same reach as the human who configured it.

● Treat every free-text field an agent’s task flow accepts as untrusted input reaching a shell. Branch names, file paths, commit messages, ticket titles — any of them can become command injection the moment they’re passed unsanitized into a subprocess, exactly as happened here.

● Prefer short-lived, single-use credentials over static tokens. A token scoped to one task and expiring when it’s done limits a successful theft to that one task, no matter how the theft happened.

● Ask for actual visibility into agent access, not a policy document about it. If your team can’t answer “what could this agent’s credential actually do right now” with a specific list of repos and scopes, the confidence gap between policy and actual agent visibility should concern you.

The Bottom Line

The Codex flaw is fixed. The pattern that made it dangerous — an agent holding more access than its task requires — remains widespread in enterprise AI deployments. Static credentials can make that problem worse by allowing compromised access to persist beyond a single task. The sanitization bug was the easy part to find. The permission scope is the part worth checking before the next one is.

September 30, 2026
Your DevOps Pipeline Is Already a Sustainability Program

During my doctoral research on modern engineering practices and operational efficiency, one pattern kept surfacing that I did not expect to find. The engineering teams making the most measurable progress on sustainability were not the ones that had appointed green committees. They were the ones running tight DevOps discipline: right-sized fleets, lean pipelines, tuned alerting, progressive rollout, carbon-aware routing. The same practices that cut cost and toil were quietly cutting emissions at the same time. Nobody was measuring it that way.

That observation became a thread I kept pulling. The more I looked at how modern engineering practices drive operational efficiency, the more I found sustainability outcomes hiding inside work that engineering teams were already doing, and already getting credit for on the cost side. The carbon savings were real. They just were not being captured. The problem is not that enterprises lack green intent. It is that sustainability is being treated as a separate program with a separate mandate, when the evidence points to it being a byproduct of engineering discipline that already exists.

Separated programs fail for a structural reason. When the sustainability initiative does not control the pipeline, the link between the action and the saving never closes, because the team driving the outcome is not the team measuring it. Meanwhile, cost and carbon move together in a way that is easy to underestimate. At enterprise scale, idle compute across hundreds of services compounds into material emissions, not rounding errors, and the savings do not stack in isolation either. Better pipelines, leaner fleets, and smarter deployments reinforce each other over time, so each practice makes the next one easier. The gap has never really been about action. Most teams are already doing the work. Few are capturing the outcome. The greenest practice is often just good engineering, measured.

Seven practices show that pattern most clearly. Each produces a sustainability outcome as a direct consequence of engineering done well. None of them require a GreenOps mandate to deliver results.

Cloud Right-Sizing and Autoscaling

Idle compute is waste an enterprise pays for and emits for at the same time. Horizontal autoscaling adds and drops instances as real demand rises and falls, and right-sizing matches instance type to actual CPU and memory profiles rather than to a conservative guess made at provisioning time. Scaling to zero lets idle services drop to nothing overnight instead of running unattended, and spot or burstable capacity absorbs fault-tolerant workloads that most organizations still run on full-price, always-on instances. At enterprise scale, the blocker is rarely technical. It is that the team owning the deployment configuration is rarely the team paying the cloud bill. Closing that ownership gap is what makes the tooling work.

Build and Pipeline Efficiency

A 30-second build saving multiplied over 500 daily builds across 50 teams is a material compute reduction, and most organizations have never measured it. Layer and dependency caching, incremental builds, smart test selection, and slimmer container images each look like housekeeping in isolation. At fleet scale, run thousands of times a day, they are compounding efficiency. The saving is not in any single build. It is in the multiplication.

Git Discipline

Undisciplined Git practices are one of the most overlooked sources of wasted CI/CD compute in large engineering organizations. Every unnecessary pipeline run, stale branch scan, and redundant trigger is compute nobody asked for and nobody noticed. Deleting merged branches, scoping pipeline triggers to the events that actually require them, avoiding redundant runs from duplicate pushes and manual re-runs, and enforcing artifact retention policies are governance decisions as much as hygiene. Every pipeline that did not need to run is compute that was paid for and emitted, and Git discipline is where that starts.

Alert-Driven Provisioning

Noisy alerts drive reactive over-provisioning, and the reason it persists is an asymmetric incentive: teams get paged for under-provisioning but face no consequence for carrying excess capacity. Killing false positives, alerting on user-facing symptoms rather than internal blips, grouping and deduplicating alert storms, and provisioning to measured load rather than to worst-case fear all change provisioning behavior, not just on-call quality. Fear-based buffers can be a major sustained source of idle compute in mature fleets, and the provisioning problem is downstream of the alerting problem.

Zombie Environment Cleanup

Orphaned development and test environments persist not because nobody thought to clean them up, but because ownership is ambiguous and deleting them requires confidence that nothing still depends on them, confidence most organizations do not have. Ephemeral environments that spin up on demand and tear down after use, a strict expiry on every sandbox with no extension unless a named owner renews it, and infrastructure-as-code reproducibility that rebuilds an environment instead of keeping it warm indefinitely all remove the reason to let one linger. Legacy environments that predate infrastructure-as-code adoption need their own remediation track before teardown is safe. A zombie environment burns compute for no one, and the fix is ownership mapping, not just automation.

Progressive Rollout and Blast Radius

Shipping to a small slice of the fleet first and ramping traffic only as health signals stay green cuts the compute cost of a failed deployment directly, since a contained failure costs a fraction of what a full rollback requires. The larger sustainability gain is second-order. When blast radius is bounded, teams no longer need to carry excess standing capacity as insurance against a full-fleet incident, so the safety buffer shrinks because the risk that justified it shrinks first. At enterprise deployment frequency, the cumulative compute saved by avoiding full rollbacks compounds significantly over time. Fewer failed deploys means less wasted compute, and a smaller blast radius means less standing capacity carried just in case.

Carbon-Aware Cloud Routing

Static region assignment is a legacy architecture decision dressed up as an operational default. Modern cloud governance can route workloads dynamically on traffic patterns, risk-tolerance windows, time-of-day load profiles, and carbon-intensity signals from providers including Electricity Maps and the cloud providers themselves. Batch and reporting workloads can shift to off-peak windows in lower-carbon regions. Failover and cutover rules built for resilience can carry carbon intensity as a weighted factor alongside latency and cost, so that when a resilience event triggers a regional shift, routing toward a cleaner grid is a policy option rather than an afterthought. The infrastructure already supports this. Most teams have not built the routing policy to use it, and framed correctly, carbon reduction arrives as the byproduct of resilience architecture rather than as a separate mandate.

The organizations that will lead on sustainable engineering will not be those that stood up the most ambitious green programs. They will be those that added carbon and compute efficiency as measured outputs of the engineering work they were already running, tracked continuously alongside reliability and cost, rather than treating it as a retrospective claim. Eliminating the waste produces both financial savings and emissions reductions, because they are the same signal measured from two directions. Right-sized fleets support better alerting thresholds, better alerting supports tighter provisioning, and tighter provisioning reduces the capacity buffers that inflate rollback blast radius in the first place. Each practice makes the next one easier. That measurement, byproduct rather than branding, is what most organizations are still missing.

September 4, 2026
When AI Coding Agents Become Malware Delivery Systems

AI coding agents are becoming part of everyday development work. Developers use them to find libraries, configure projects, troubleshoot installation problems, and set up new tools. An agent can search GitHub, read project documentation, and run commands without the developer having to work through every step manually.

That convenience is creating another security concern for DevOps teams. An agent can encounter software and instructions during a task, decide that they are relevant, and act on them. A repository, README file or configuration file can therefore become part of the execution path.

Recent attacks have shown how this can be abused. Malicious repositories have been created to attract AI coding agents, while researchers have demonstrated attacks that use project instructions to influence what an agent downloads or executes. Once an agent has access to a terminal, the filesystem, or the network, a routine development task can provide malicious code with a way into the environment.

That raises a difficult question: how much trust should an AI coding agent be given when deciding what software to run?

AI Coding Agents Have Changed How Developers Install Software

A developer who wants to add a new tool can now ask an AI coding agent to handle most of the work. The agent can find a repository, read the setup instructions, install the required packages, and configure the project. That works well when the repository is trusted. The risk increases when the agent has to decide which repository to use or which instructions to follow.

Consider a simple request to add an MCP server. The agent may search GitHub, look at several repositories, choose one that appears relevant, and follow the installation instructions in its README. It can then modify configuration and run the commands needed to get the server working.

The developer may approve the final command without seeing everything that happened before it. The agent may have already picked up instructions from a source the developer hasn’t fully evaluated and followed them as part of the task. By the time the developer sees the result, those instructions may already have influenced what the agent downloaded or executed.

The agent is doing exactly what it was asked to do. The problem is that the instructions it found along the way came from somewhere the developer never explicitly trusted. This is where AI-assisted development starts to create a different kind of software supply-chain problem. The attack can begin before the code reaches the repository, the build server or the production pipeline.

A README Can Now Be Part of the Attack Surface

README files have always been a normal part of setting up a project. They tell developers how to install dependencies, configure the environment, and get the code running.

A June 2026 proof of concept from Mozilla’s Zero Day Investigative Network showed how a repository could manipulate a coding agent into opening a reverse shell. The repository contained instructions for initializing a fictional Python tool. After an expected first-run error, the agent followed the suggested troubleshooting steps, eventually retrieving encoded instructions from a DNS TXT record and passing them to a shell. The demonstration shows how indirect prompt injection attacks can turn otherwise ordinary project content into instructions for an AI coding agent.

The malicious payload wasn’t sitting in the repository in an obvious form. It appeared later in the execution process, after the agent had already started following the project’s instructions. That makes a command approval less useful on its own. A developer may see a familiar setup command without knowing everything that command will eventually execute.

README files aren’t the only place where agents can pick up instructions. AGENTS.md, contribution guides, issue descriptions, code comments, and tool-specific configuration files can also influence an agent’s actions. GitHub’s research into prompt injection in VS Code has shown how untrusted content brought into an AI-assisted development workflow can influence an agent and potentially expose files, tokens, or command execution. Once an agent can read project content and act on what it finds, documentation becomes part of the execution path.

Malicious Repositories Don’t Need to Look Malicious

AI coding agents are increasingly being used to find software and tools for developers. That makes the search process itself an interesting target for attackers.

The FakeGit campaign is a good example. Researchers identified thousands of malicious GitHub repositories posing as legitimate AI tools, skills, and MCP servers. Some used familiar project names, copied repository layouts, and other details that made them look credible. The repositories ultimately delivered malware, but the important part of the attack happened earlier: the attackers first had to make the projects discoverable and convincing enough for an AI system to recommend them.

Some of these repositories were also surfaced by AI coding agents during ordinary software searches. An agent looking for a particular capability could encounter a malicious repository, treat it as a legitimate option, and pass its installation instructions to the developer. Researchers referred to this technique as AgentBaiting.

For developers relying on agents to find software, discoverability has become part of the security problem. A malicious project that reaches an AI agent at the right moment can get much closer to execution than one waiting for a developer to find it manually.

Why Traditional Security Controls Can Miss the Problem

Traditional security tools still have an important role in protecting developer environments, but they don’t always see the full chain of what happens after an agent starts working on a project.

A command can come from a legitimate shell, a trusted development tool can execute it, and the repository itself can pass a basic security check. The problem may only become visible when those actions are considered together. That makes the distinction between antivirus and malware defenses important. Antivirus tools can identify known malicious files, while broader malware protection can also look for suspicious behavior. Those controls remain useful, but they don’t explain how the software reached the machine or why it was executed.

An agent can find a repository, read its instructions, and run a command as part of a task the developer requested. From the operating system’s perspective, the command may still look like ordinary development activity.

The context behind that command can be harder to see. If the initial trigger came from a repository, project file or instruction that the agent treated as trustworthy, a security tool may only see the final action rather than the sequence that led to it.

Traditional endpoint protection can detect malicious files or suspicious behavior, but it may have limited visibility into the instructions and decisions that caused the code to run.

What DevOps Teams Should Change

The first step is to treat the agent’s access as part of the development environment’s security boundary. An agent that can install packages, modify files, and execute shell commands has considerably more reach than one that can only suggest code. That access should match the task. An agent reviewing a pull request doesn’t need the same permissions as one preparing a release or configuring a development environment. Keeping those boundaries separate limits what can happen when an agent follows an instruction it shouldn’t.

The same applies to software discovery. Repositories, packages and MCP servers found by an agent should go through the same trust checks as anything a developer would install manually. Popularity, repository activity or an agent’s recommendation shouldn’t be treated as proof that a project is safe.

Execution also deserves more attention. Running agents inside isolated environments can limit access to credentials, sensitive files and production systems. Network restrictions can provide another layer of protection when an agent needs to download dependencies or communicate with external services.

Teams also need better visibility into what agents actually do. Logs should capture repository sources, commands executed, packages installed and permission changes. That makes it easier to investigate an incident when the final malicious action looks like an ordinary development command. Clear permissions, trusted software sources, and isolated execution environments can limit how far an attacker can move when an agent follows a malicious instruction.

Conclusion

AI coding agents are becoming part of the software development process, and the way they discover and execute software deserves more scrutiny. Attackers are already experimenting with malicious repositories, project instructions, and other content that can influence what an agent does. As these tools gain more access to developer environments, the consequences of a bad decision become larger.

The development workflow is changing, and so is the attack surface around it. An agent can now make decisions about software, follow instructions from project content and execute commands with very little human involvement. That makes the trust placed in these systems an increasingly important part of software security.

August 27, 2026
The Rise of AI-Native DevOps: How AI Is Reshaping Software Delivery in 2026

For years, DevOps had a pretty straightforward mission: help teams ship reliable software faster by getting development and operations folks working together. Tools like automation, continuous integration, continuous delivery, infrastructure as code, and better observability have all been key pieces of the puzzle. But now, things are shifting again. This time, artificial intelligence is changing the whole landscape.

AI isn’t just some fancy code-completion tool anymore. Modern engineering teams are building it into almost every stage of how they deliver software. From writing deployment pipelines, to troubleshooting incidents, and analyzing data, AI is stepping into each corner. People are starting to call this new wave “AI-Native DevOps.” In this world, AI’s not just here for a productivity boost. It’s actively involved in pushing software out the door.

The change might sound subtle, but it’s actually a pretty big deal. Old-school DevOps relied on automation, sure, but it all followed predefined rules. These new AI-powered systems go further. They get the context, spot patterns, offer suggestions, and sometimes even take action without much help from a person. That’s making teams rethink what automation even means.

Take continuous integration, for example. Before, the pipeline just checked if code could build, if the tests passed, and if the quality gates were hit. Now, AI digs deeper. It looks at where things have failed before, flags risky changes, and even tells you which pull requests really need your attention. Instead of treating every code change the same, AI helps teams zoom in on what matters most.

You see the same thing happening with deployments. Teams used to rely on set rules. If the tests pass and everything builds, then maybe someone checks it off and out it goes. AI’s changing that. It sifts through signals like past release trouble, service dependencies, infrastructure health, and previous incidents. It doesn’t take the decision away from humans, but it gives teams more insight before they ship something live.

Then there’s incident management. When stuff breaks, engineers usually lose time chasing logs, poking through dashboards, or searching documentation just trying to find what went wrong. AI can pull data from all those different tools, tie things together, and even hand over a shortlist of likely causes. People still need to check its work, but getting to the root problem? That’s way faster now.

Observability tools are changing too. Nowadays, monitoring systems collect ridiculous amounts of data. Figuring out what actually matters? That’s never been easy, especially as everything gets more spread out. AI helps spot weird patterns, connects events from different services, and filters out the noise. Instead of hitting teams with a flood of pointless alerts, these new tools highlight just the ones that really deserve a response.

Infrastructure management’s seeing a shift as well. Infrastructure as Code is still the go-to for running cloud setups, but keeping all those environments clean and secure is a grind. New AI assistants can flag security gaps, spot inconsistent configs, suggest improvements, and even whip up code templates to match your architecture. Engineers still review everything before it goes live, but the whole process gets easier and less error-prone.

Maybe the biggest change is the rise of AI agents. Unlike old assistants that just answer when called, these agents can run entire workflows within safe limits. Say there’s a performance dip. An operations agent might spot it, pull diagnostics, check for similar past incidents, put together a fix, and ask a human for the green light. It’s a big leap from the kind of automation where you had to program every single step ahead of time.

Even with all this progress, AI-Native DevOps isn’t just about cranking up automation. Trust is still at the heart of everything. Production needs things to work as expected, and teams need real accountability. AI’s suggestions only matter if people understand the “why” behind them. So, clear approval paths, audit trails, and tight permissions aren’t optional. They’re the rule before any AI system makes an operational call.

Data quality matters too. AI’s only as smart as the information it gets. Weak documentation, flaky monitoring, mislabeled incidents, or bad operational data? That just makes the AI less helpful. Teams looking for real value often have to clean up their own processes with better documentation, improved monitoring and tighter discipline before the AI can do any heavy lifting.

Security’s another big deal. These AI tools need deep access: repos, pipelines, cloud environments, monitoring, the lot. If you’re not strict about identity and privilege, all that integration piles on new risks. Security needs to be baked in from the start, not added at the end.

DevOps engineers are feeling this shift firsthand. Instead of grinding away at repeatable tasks, they’re expected to review AI’s work, set the guardrails, keep operational data solid, and make sure automation’s reliable. Their focus is shifting from doing routine chores to supervising smarter systems and making bigger-picture decisions.

Don’t get the wrong idea. AI isn’t replacing DevOps pros. Production systems are messy, business needs change on a dime, and chaos never goes out of style. There’s no substitute for hands-on experience and solid engineering judgment, especially when things hit the fan.

What’s happening is DevOps is just moving up to the next level. Automation’s still huge, but what we call “automation” is evolving, getting smarter and more aware of the bigger picture. Teams that mix AI’s strengths with strong engineering, real guardrails, and sharp human oversight will come out ahead.

In the end, it’s not about how many AI tools you stack up. The real winners are building a kind of partnership, letting smart systems boost their own expertise rather than trying to replace it. As software delivery keeps getting more complex, finding the right mix of artificial intelligence and human common sense might end up defining the best DevOps teams in the years to come.

August 11, 2026
Open Source Code Just as Secure as Proprietary Software—If You Manage It Right, Says CISA
Open source can be just as safe as proprietary software, though government agencies (and private enterprises) should take additional measures to secure it properly, according to a new guide published by the U.S. Cybersecurity and Infrastructure Security Agency (CISA).

The report, “Open Source Software: Security Principles and Practices,” provides with guidance to help agencies comply with two Presidential Executive Orders (14144 and 14306) which both urged them to do a more thorough job securing the software they use, a response to recent exploits like Log4Shell and XZ utils.

CISA acknowledges that open source can provide immeasurable benefits to government agencies, not merely because it is available without licensing fees, but also because agencies can extend the software to meet their own requirements. But at the same time, open source software must be managed differently than proprietary software.

Malicious hackers (and increasingly AI agents) use vulnerabilities to gain entry and exploit systems. In the case of the vendor software, it is the vendor’s responsibility to provide timely updates should a software vulnerability be found, which agencies should apply as soon as possible.

With open source software, however, agencies need to take a more proactive role in patching and understand the dependencies, or additional software components, that an open source package relies on. Those components also need to be secured, the guidance advises.

The guidance describes established principles for patching. It also provides a framework to evaluate trustworthiness and risk tolerance of software packages, as well as a set of best practices to procure, deploy and maintain open source software.

In practical terms, agencies wishing to use open source should establish a process that supports staff in choosing the software that meets the agency’s needs but minimizes risks to the agency.

For instance, an agency’s Office of the Chief Information Officer may set up a system to pre-approve certain software libraries for use in the agency. Automated tools could help verify safe software packages, and reviews would consider potential risk exposure. The CIO could still individually sign off on higher-risk components, such as operating systems.

To help agencies, CISA produced the C4 Framework, which can be used to assess the trustworthiness of a software package. It looks at four factors (the four Cs):
- Codebase: Take a look at the actual source code and dependencies that make up the software component of interest. How recently was the last update committed? How many vulnerabilities have been found? How many out-of-date dependencies does it rely on?
- Community: How robust is the community that maintains and contributes to the project? Is it part of a foundation, or managed by a private company?
- Conduct: How is the project managed? Is there a vulnerability disclosure process? Do the project leaders follow their own code of conduct?
- Configuration: Is the default configuration secure? Are there guides for use in highly-sensitive environments?
Open source AI systems should go through additional checks, CISA recommends. Agents should get sufficient transparency into the training data, in addition to the software itself. “Only with transparency and access can agencies understand and study the software, analyze it for vulnerabilities, and remediate any found vulnerabilities or risks,” the guide recommends.

CISA has targeted this report to U.S. federal agencies, though the advice here is rock-solid for use in the industrial sector as well. Many businesses in finance and health care, for instance, routinely follow government guidance. So if your organization uses open source in any capacity, this report may be worth checking out.
July 31, 2026
Why Log Monitoring Is the Missing Link in Most Incident Response Workflows

Modern engineering teams have invested heavily in observability. Dashboards are populated, alerts are configured, on-call rotations are set. Yet when production incidents occur, the average time to resolution hasn’t dropped nearly as fast as the tooling has improved. The reason, in many cases, comes down to one overlooked gap: logs aren’t being used the way they should be during active incidents.

This isn’t a tooling problem. Most organizations already have log data flowing somewhere. It’s a workflow problem, specifically, how logs integrate (or fail to integrate) into the broader incident response process.

The Three-Tool Problem

A typical incident response scenario looks something like this: an alert fires, the on-call engineer opens a metrics dashboard to understand the scope, then switches to a separate logging tool to investigate root cause, then jumps to a tracing tool to follow the request path. Three tools. Three contexts. Three sets of query syntax.

By the time the engineer has assembled enough context to diagnose the issue, significant time has already elapsed, not because the data wasn’t there, but because it wasn’t connected. According to a 2025 survey of IT and DevOps leaders, nearly one-third of organizations use five or more observability tools, and two-thirds report it takes four or more hours to resolve production issues. The correlation isn’t coincidental.

Logs as Narrative, Not Just Data

The fundamental value of logs in incident response isn’t just diagnostic; it’s narrative. Metrics tell you that something is wrong. Traces tell you where in the system the failure is propagating. Logs tell you what actually happened, line by line, at the exact moment things broke. That narrative context is irreplaceable during high-pressure incident triage.

The problem is that logs are often treated as a post-incident forensics tool rather than a real-time response asset. Teams pull logs after the fact to write postmortems, not during the incident to accelerate diagnosis. Flipping this pattern, making log data immediately accessible and correlated with other signals at the moment of detection, is one of the highest-leverage changes a team can make to their incident response workflow.

What Effective Log Integration Actually Looks Like

The shift from reactive to active log usage in incident response comes down to three practices.

First, log correlation at the point of alerting. When an alert fires, the response interface should surface relevant log events automatically, not require the engineer to open a separate tool and construct a query from memory while under pressure. Correlated log context should be part of the alert payload itself.

Second, structured logging as a standard, not an aspiration. Unstructured logs are difficult to query quickly under pressure. Teams that have invested in structured logging, consistent field names, log levels, service identifiers, and request IDs dramatically reduce the cognitive load on responding engineers. Searching for a specific request ID across services becomes a seconds-long operation rather than a minutes-long one.

Third, the right tooling for the volume and velocity of log data your system generates. Choosing and configuring log monitoring tools that can ingest, index, and surface logs in real time, rather than with a lag, is foundational. A logging platform that’s five minutes behind during a P1 incident is functionally useless for triage purposes.

The On-Call Experience Is a Product Decision

One underappreciated dimension of log monitoring in incident response is its impact on on-call engineer experience. Alert fatigue is a well-documented problem, but log fatigue, the experience of drowning in raw, unstructured, uncontextualized log data during an incident, is equally damaging and far less discussed.

When logs are well-structured, correlated with metrics and traces, and surfaced at the right moment, on-call becomes a manageable cognitive task. When logs are siloed, noisy, and queryable only through a separate interface, on-call becomes an exhausting detective exercise that degrades both resolution speed and team morale. The investment in better log monitoring isn’t just an observability decision, it’s an organizational health decision.

The OpenTelemetry Factor

The growing adoption of OpenTelemetry is quietly reshaping how teams approach log integration. As OpenTelemetry’s logging specification matures, teams are gaining a standardized way to collect, enrich, and export logs alongside traces and metrics using a single, vendor-neutral instrumentation layer. This matters for incident response because it creates the technical foundation for true correlation, logs that carry the same trace IDs as distributed traces, enabling engineers to move seamlessly between signal types during triage.

Teams that invest in OpenTelemetry-native instrumentation now are building an incident response capability that compounds in value over time as their systems grow more complex.

Closing the Loop

Incident response maturity is often measured by MTTR, mean time to resolution. But MTTR is a downstream metric. The upstream inputs that actually move it are time to detection, time to context, and time to diagnosis. Log monitoring, done well, directly compresses all three.

The teams shipping the most reliable systems aren’t necessarily the ones with the most sophisticated alerting or the most granular metrics. They’re the ones who have made log data a first-class citizen in their incident response workflow, not a fallback resource for postmortems, but an active, correlated, real-time signal that shortens the gap between something breaking and someone understanding why.

In a world where every minute of downtime carries a measurable cost, that gap is worth closing.

July 31, 2026
The Innovation Puzzle 2.0: Connecting Vision and Value

The tech landscape constantly evolves, making it tempting to chase every new trend. Ironically, in today’s business world, the word “innovation” has become almost synonymous with technology adoption. Yet after years of leading digital transformations, I’ve learned that sustainable innovation isn’t about having the latest technology—it’s about solving business problems and delivering measurable impact.

In 2025, the hype was “we’re building an AI strategy.” In 2026, it’s becoming clearer that many of those strategies were more aspirational than actionable—some even serving more as positioning than practical guidance. The latest wave of generative AI has carried a magic-wand promise: that simply generating outputs would unlock transformation overnight. But enterprises are quickly realizing that generating output was never the hard part. The real challenge—and the real differentiator—is governing it responsibly, securing it end-to-end, controlling its behavior, making it cost-effective, and scaling it sustainably across the enterprise.

Building a Foundation for Tomorrow

When executives talk to me about building innovation capabilities, conversations often jump straight to technology investments. While modern tools and platforms play a role, successful organizations understand that innovation is built on a broader foundation. It requires a deliberate balance between modernizing systems, improving experiences, and empowering people. This holistic approach helps organizations avoid the common trap of implementing technology without purpose.

A 2026 enterprise AI adoption study reinforces that the real gap in innovation isn’t experimentation—it’s underestimating foundational readiness across data, architecture, and operating models

Core Modernization: The Technical Base

Most transformation initiatives fail not from choosing the wrong technology, but from not first asking what problem needs solving. The goal isn’t to eliminate all legacy systems—it’s to identify which modernization efforts will drive meaningful business outcomes. One large financial services organization learned this during its AI-driven transformation: before scaling personalized AI recommendations, it first modernized fragmented backend data spread across legacy systems, enabling faster onboarding and improved customer servicing. Success in modernization requires focus in two key areas:

1. System Transformation

Great enterprise technology is never just about what’s under the hood. Apple proved this, their edge was never the most features, but making powerful technology feel effortless. The same holds for enterprise modernization: architecture without adoption is just expensive infrastructure. Enterprises that get this right think in three layers from day one:

Architecture-first → Define how systems connect, where data lives, where AI fits, and how governance is enforced. Every modernization decision that follows should align to this blueprint, without it, teams modernize in isolation and recreate the fragmentation they were trying to solve.

API-first as the connective tissue → APIs are the boundaries between capabilities, not just integration points. Design them to be governed, versioned, secured, and observable. This clean separation also makes the experience layer possible: backends evolve without disrupting what users see, and new experiences are assembled without rebuilding the systems underneath.

Experience Layer: Bridging UX and Business Workflows → A large retail bank improved its digital experience by first building an Experience API layer to unify fragmented backend systems into consistent, real-time customer journeys. This reduced delays in key user flows, significantly improving UX. Mobile app ratings, customer satisfaction scores, and self-service adoption all increased as a result. The key shift wasn’t AI—it was fixing the experience foundation first through APIs.

The experience layer delivers when it gets two things right:

Adaptive interfaces: Personalization and self-service at scale, across roles, contexts, and form factors

Design elegance: Users accomplish tasks without friction; voluntary adoption is the proof

Architecture gives you the blueprint. APIs give you the boundaries. The Experience Layer gives you adoption.

2. Developer Acceleration

AI-assisted development tools have fundamentally changed what “fast” means. With tools like GitHub Copilot and others, generating code is no longer the bottleneck – knowing whether that code is correct, secure, scalable, and architecturally sound is. The risk enterprises face today isn’t moving slowly; it’s accelerating without the controls to ensure what’s being built is the right thing, built the right way.

Real development acceleration means teams can build quickly and confidently. That requires:

Composable platforms with architectural guardrails → assemble capabilities from governed, reusable building blocks validated against your target architecture, security policies, and compliance requirements

CI/CD as a quality and governance gate → automated pipelines enforce code quality, security scanning, dependency checks, and architectural compliance at every commit; this is where accountability lives

Reusable components with clear ownership → components are built with ownership; maintained, versioned, performant, scalable, and accountable

Sustainability and maintainability as non-negotiables → ensuring code remains consistent, modular, upgradeable, and operable at enterprise scale, the bar for what gets merged must go up

The mindset shift: AI tools make developers dramatically more productive. The enterprise challenge is ensuring that productivity compounds, what’s built today must be an asset tomorrow, not a burden.

The ROI of Modern Innovation

Innovation becomes meaningful only when it translates into measurable operational efficiency, stronger customer engagement, and tangible business outcomes.

1. Operational Metrics

AI workflow efficiency → reduction in human effort per process, measured by time and cost at scale

Time-to-market acceleration → from idea to production; how much faster teams deliver validated, production-ready capabilities compared to baseline

Infrastructure and operational cost optimization → cloud spend, API consumption, and AI inference costs managed against delivered business value; scaling output without scaling cost proportionally

2. User Impact

End user adoption → better UX and personalized experiences drive higher engagement, satisfaction, and voluntary adoption

Team productivity gains → teams delivering more, faster, with fewer dependencies and handoffs

Self-service success rate → users independently completing tasks and workflows through the platform without needing support or intervention

3. Business Outcomes

Revenue impact → new capabilities, faster product delivery, and personalized experiences directly enabling new revenue streams or expanding existing ones

Customer satisfaction and retention → experience quality and self-service capability translating into measurable NPS gains, reduced churn, and stronger customer relationships

Operational agility → ability to respond to market changes, launch new capabilities, and adapt to customer needs faster than competitors; the compounding business advantage of a modern foundation

The AMVC Framework: Bridging Intent and Business Impact

Innovation initiatives often stall not from lack of vision or technology, but from the disconnect between AI implementation and measurable business value. Across industries, organizations have successfully launched AI pilots yet struggled to operationalize them securely, responsibly, and at enterprise scale.

After seeing this pattern repeatedly, I advocate a simple but powerful framework – AMVC: Align, Measure, Validate, and Communicate – to bridge the gap between innovation intent and sustainable business impact. It ensures AI investments remain outcome-driven, governable, and adaptable as business needs evolve.

1. Align: Connect AI initiatives to clear business priorities, define governance boundaries and human oversight requirements upfront, establish operational readiness, and secure stakeholder ownership.

2. Measure: Track not just technical performance, but adoption, operational efficiency, customer impact, and business outcomes. Define clear success criteria that include security and compliance adherence. Track unknowns and emerging risks-how an organization identifies, accepts, and systematically eliminates them is a measure of institutional maturity.

3. Validate: Continuously test with end users, monitor AI behavior against defined boundaries, and validate that outputs are reliable, consistent, and trustworthy at scale—not just in the controlled conditions of a pilot.

4. Communicate: Share progress, lessons learned, and measurable outcomes across the organization. Be transparent about what the AI cannot do and where human judgment remains in control. Momentum requires trust, and trust requires transparency about boundaries as much as capabilities.

Building an Innovation Culture That Scales

Technology and frameworks may create the conditions for innovation, but culture determines whether transformation truly succeeds. The most advanced architectures and AI capabilities will fail to deliver value if people don’t trust them, understand them, or feel empowered to question and improve them.

Building a scalable innovation culture requires focus in three areas:

Enable True Collaboration: Innovation cannot live within technology teams alone. Sustainable transformation happens when business, operations, product, risk, and technology teams work through continuous feedback loops. The best organizations make collaboration part of the operating model, not an exception.

Build Capability Through Practice: AI lowers the barrier to building, but raises the bar for judgment. Organizations must invest not only in tools, but in helping teams critically evaluate AI outputs, understand trade-offs, and innovate responsibly. Empower citizen developers with the right guardrails, encourage rapid experimentation, and treat lessons from failure as institutional knowledge.

Take Calculated Risks Responsibly: In 2026, innovation requires balancing speed with discipline. Mature organizations know when to experiment aggressively and when to pause until governance, security, or operational readiness catches up. Leaders who model both create a culture that can innovate confidently and sustainably at scale.

The Path Forward

AI is evolving faster than any single framework, governance model, or technology investment can fully anticipate. That’s not a reason to wait, it’s a reason to build with intention.

The organizations that will lead the next wave of innovation are not necessarily those with the most advanced AI, but those that invested early in the right foundations – architecture, governance, scalability, and experience design – before speed made shortcuts tempting. They built cultures where capability and accountability grew together, and they measured what truly mattered to the business, not just what was impressive to demo.

The innovation puzzle was never just about technology. It has always been about discipline – the discipline to align every investment to a business outcome, validate before scaling, communicate with transparency, and build systems your organization can trust, operate, and evolve.

The pieces are already on the table. The question is whether the foundation exists to bring them together.

The future belongs not to those who chase every trend, but to those who build sustainable innovation capabilities that deliver lasting value.

July 27, 2026