DevSecOps Best Practices in the Age of AI

Why DevSecOps Must Evolve for AI

Traditional DevSecOps practices have delivered significant gains in automating security testing, shifting security left, and embedding guardrails throughout the CI/CD pipeline. However, as development teams adopt AI and machine learning, cracks in the model have begun to show. Standard AppSec tooling doesn’t account for risks like adversarial prompts, poisoned datasets, insecure plugins, broken or insecure access controls, emergent model behaviors, or additional supply chain vulnerabilities. Notebook-based workflows often escape version control and security policy enforcement. And most pipelines lack controls for the dynamic, data-centric nature of AI systems.

These limitations are not due to flaws in DevSecOps itself, but rather its design assumptions — that code is deterministic, flows are reproducible, and the primary risks lie in traditional vulnerabilities. In AI, these assumptions no longer hold. Models can change behavior depending on subtle data shifts. APIs may expose sensitive outputs without any underlying CVE. Threats evolve post-deployment.

This is where AI can also become part of the solution. AI- and Agentic AI-powered DevSecOps tools can analyze massive codebases to detect security flaws, triage alerts more intelligently, and even recommend secure code fixes. Just as AI complicates security, it also enhances our ability to automate and scale security operations.

This guide explores how to evolve your DevSecOps strategy to both secure and leverage Agentic AI. We’ll examine best practices, implementation techniques, and success criteria for building a modern DevSecOps program ready for Agentic AI.

1. Understand the Role of AI & Agentic AI in Your Stack

Before teams can secure AI, they must understand how and where it operates. Are you building proprietary machine learning models? Using APIs from large model providers like OpenAI? Or incorporating AI into DevOps automation tools? The risks differ widely depending on the answers.

Best Practice: Start by mapping your AI footprint. This means identifying all the points where AI is embedded, from internal notebooks and pipelines to customer-facing features. It also means understanding who owns the AI workflows (e.g. developers, data scientists, or a hybrid of both). Asset inventories should include datasets, pre-trained models, pipelines, and endpoints that expose AI services. Agentic AI can automate this discovery process, continuously crawling codebases, configurations, and pipeline metadata to generate and maintain up-to-date AI component inventories, closing visibility gaps at enterprise scale.

Tips: Use threat modeling frameworks like STRIDE or MITRE ATLAS to assess each AI component’s risk exposure. Map out the entire data flow — from raw ingestion to model inference — and identify security controls (or the lack thereof) at each stage.

Goal: By aligning stakeholders and creating a high-level threat model early, you’ll be better equipped to prioritize and secure AI touchpoints. This foundational visibility allows DevSecOps teams to determine which assets are critical and need controls first, as well as which may introduce third-party or regulatory risks.

2. Secure the AI Development Workflow

AI workflows often bypass traditional software engineering rigor. Jupyter notebooks, ad hoc datasets, and untracked models are still common. Left unchecked, these practices invite vulnerabilities, data exposure, and compliance violations.

Best Practice: Bring AI into your established DevSecOps pipeline. Ensure that AI code is versioned, models are tracked, and deployments are gated through CI/CD just like any other service. Create repeatable pipelines for training, testing, and promoting models between environments. Agentic AI can enforce policy-driven checks throughout the model lifecycle, acting as autonomous reviewers to catch misconfigurations, insecure model parameters, or missing audit trails before they reach production.

Tips: Leverage MLOps tools like MLflow or Kubeflow to enforce model lineage and enforce reproducibility. Use Git LFS to track large artifacts and ensure that any model pushed to production is traceable to the original training dataset and configuration.

Goal: Security thrives in structure, and AI needs it more than most. By enforcing code reviews, dependency scanning, and artifact traceability in your model lifecycle, you make it possible to apply the same governance and auditability expected from traditional software.

3. Protect AI-Specific Attack Surfaces

Unlike traditional software, AI systems are often probabilistic and data-driven. This makes them vulnerable to threats that don’t exist in classical application code like prompt injection, model inversion, or data poisoning.

Best Practice: Safeguard your systems by simulating how attackers might misuse them. Perform adversarial testing using tools like IBM’s Adversarial Robustness Toolbox or Microsoft’s Counterfit. Build LLM-specific controls such as prompt sanitization and output filtering for public-facing model interfaces. Prompt sanitization involves inspecting and cleaning user inputs to LLMs or AI services to prevent injection attacks that could manipulate model behavior, exfiltrate data, or bypass safety constraints. Output filtering ensures that generated responses are reviewed — either manually or through automated tools — to detect and suppress harmful, sensitive, or policy-violating content before it reaches users, particularly in customer-facing applications. Agentic AI can be deployed to continuously scan exposed model endpoints in staging environments, generating adversarial inputs and analyzing outputs for sensitive information leakage, misbehavior, or safety violations at scale and without manual intervention.

Tips: Validate inputs to LLMs for context tampering, payload obfuscation, and attempts to override instructions. Monitor outputs for known patterns of sensitive information leakage, compliance-violating content, or hallucinated data. Fine-tune models on adversarial examples to improve resilience.

Goal: Establish a hardened AI interface that prevents malicious input manipulation and restricts harmful or sensitive model output before it reaches users. By focusing on input sanitization and output filtering, organizations can reduce the risk of prompt injection, data leakage, and reputational harm from unmoderated LLM responses.

4. Build in Secrets Hygiene and Secure Access

Modern AI systems often rely on a web of credentials: API keys for model access, cloud tokens for GPU instances, and database passwords for training sets. Unfortunately, it’s all too easy for these secrets to end up hardcoded in a notebook or config file.

Best Practice: Detect exposed secrets before they reach production and enforce secure management of sensitive information. Use automated scanning tools like Checkmarx Secrets Detection, Gitleaks, or truffleHog to scan repositories and CI pipelines for credentials, API keys, or tokens. Implement Git pre-commit hooks and CI/CD pipeline gates to block merges when secrets are detected. Enforce organizational policies through configuration-as-code, such as preventing secrets from being committed in the first place using git-secrets or custom regex matchers. Additionally, educate developers on best practices for managing secrets, and integrate secure vaulting solutions (e.g., HashiCorp Vault, AWS Secrets Manager) for dynamic, role-based provisioning of secrets during build and runtime. Agentic AI proactively scans every pull request, flagging secrets with contextual explanations and recommending secure storage alternatives, creating scalable, intelligent feedback loops during development.

Tips: Establish periodic audits of repositories and cloud environments to detect legacy or shadow credentials that automated tools might miss. Encourage secret-lifecycle reviews during sprint retrospectives or release cycles. Integrate developer feedback into tooling decisions to improve adoption and reduce false positives. Strengthen incident detection by correlating secret usage patterns in logs with code changes to catch misuse early.

Goal: Minimize the risk of unauthorized access by preventing hardcoded secrets and enforcing dynamic, least-privilege credential management. By eliminating secrets sprawl and tightening access controls, teams can reduce the blast radius of credential compromise across AI infrastructure and sensitive training data.

5. Shift Security Left in AI-Enhanced CI/CD

The beauty of DevSecOps lies in its early feedback loops. By scanning code for vulnerabilities before it’s merged, or enforcing policies before infrastructure is provisioned, you eliminate risk without slowing developers down.

Best Practice: Apply these same principles to AI. Subject any code, pipeline, or infrastructure that supports AI systems to your existing AppSec tooling. Use Static Application Security Testing (SAST) and Software Composition Analysis (SCA) on model training scripts and container images, and ensure infrastructure-as-code policies are enforced during provisioning. Agentic AI automatically enforces these policies in every build, evaluates compliance status, and even auto-generates remediations, therefore preventing security debt from accumulating across hundreds of pipelines.

Tips: Automate evaluation pipelines to include not just static and dependency scans, but also behavioral validation in controlled container environments. Use adversarial testing frameworks like SecML or IBM’s ART to simulate real-world misuse cases. Integrate policy-as-code tools such as OPA or Sentinel to enforce that models and containers meet security and performance thresholds before deployment. These measures ensure AI components not only pass traditional checks, but also behave securely under realistic conditions.

Goal: Ensure AI components follow the same rigorous security processes as the rest of the software stack by integrating them into early-stage CI/CD controls. This approach guarantees that potential vulnerabilities in model code, dependencies, or infrastructure are identified and mitigated before deployment, just like with traditional application assets.

6. Educate & Empower Developers and Data Scientists

Security only scales when people understand it. But in many orgs, the people building AI aren’t familiar with AppSec principles. At the same time, security teams may not fully grasp the nuances of model development.

Best Practice: Bridge this gap through education and collaboration. Make security education a continuous initiative across engineering and data teams by embedding it into onboarding, sprint planning, and regular technical workshops. Rotate security champions through AI and data science teams to foster bidirectional learning. Encourage shared ownership by incorporating security metrics into team objectives and recognizing proactive risk mitigation efforts. Agentic AI serves as a real-time reviewer or tutor, explaining security risks in context as developers write AI-related code. This provides instant, scalable knowledge transfer tailored to each developer’s skill level.

Tips: Rather than relying on passive slide decks or generic AppSec training, make security come alive through hands-on exploration of real AI threats. Imagine walking a team through a simulated prompt injection attack, showing them how a subtle input trick can leak model training data. Or sitting down with data scientists to dissect a misconfigured inference API that exposed credentials due to lax IAM settings. Supplement these experiences with live threat modeling sessions and a shared, evolving playbook that documents security decisions in context. When teams can see the impact of insecure AI firsthand and have a resource they trust to guide them, secure practices become second nature.

Goal: Build a security-first culture by embedding AI-specific security practices directly into how engineering and data science teams work. When all contributors understand and apply secure-by-design principles, security becomes scalable, proactive, and resilient — not just a compliance checkbox.

7. Monitor AI in Production

We know that many AI attacks don’t happen at build time; they can also happen after deployment. For example, an attacker submits a malicious prompt, or a user finds a way to manipulate model behavior. Without visibility, these issues go undetected.

Best Practice: Make runtime monitoring a priority. AI models, especially those exposed via APIs or embedded in customer-facing features, must be monitored for abuse, data leakage, and performance drift. Agentic AI ensures security at runtime, even at cloud-scale, by continuously analyzing telemetry from model inputs/outputs, detecting anomalous behavior, and triggering containment actions autonomously.

Tips: Measure what matters to model behavior and misuse detection. Tracking request frequency and prompt complexity can help detect brute-force probing, while entropy or confidence scores may reveal hallucinations or degraded output quality. Picture a sudden spike in low-confidence completions late at night; that’s your signal something odd is happening. The real power comes when this telemetry feeds into your Security Information Event Management (SIEM) for unified alerting. That way, security teams aren’t flying blind when models start acting unpredictably.

Goal: Establish real-time visibility into AI system behavior to catch anomalies the moment they arise. With proactive monitoring in place, security teams can quickly identify when a model deviates from expected patterns, enabling immediate investigation and response to threats, data leaks, or misuse before they escalate into critical incidents.

8. Respond to AI Threats with Agility

Incidents involving AI require a different playbook. You may need to roll back a model version, retrain with clean data, or block a third-party API integration. Traditional response plans often aren’t built for these scenarios.

Best Practice: Update your incident response plans to include AI-specific risks. This includes response procedures for compromised models, poisoned data pipelines, and unintended model behavior in production. With Agentic AI, developers and AppSec engineers are able to rapidly identify the origin of a compromised model, suggest rollback targets, and simulate blast radius assessments, accelerating IR workflows from hours to minutes.

Tips: Think of your AI deployment pipeline like a flight system. When turbulence hits, you need multiple layers of control to recover quickly. Maintain a versioned model registry not just for traceability, but so you can instantly roll back to a stable checkpoint if something goes wrong. Use feature flags to control which parts of a model are exposed in production, giving you the ability to throttle, isolate, or disable risky behavior in real time. And when the dust settles, bring in compliance and data governance teams to assess what was exposed and whether re-certification or data purging is necessary. These are your AI system’s circuit breakers.

Goal: Build a rapid-response capability tailored for AI incidents, so your team can act swiftly and decisively when things go wrong. With well-defined AI-aware incident response protocols in place, teams can isolate issues, limit exposure, and restore systems quickly. This ensures security, stability, and customer trust even when models behave unpredictably.

9. Define Success and Continuously Improve

You can’t secure what you don’t measure. As you build out your AI and DevSecOps posture, track key metrics: How quickly you remediate vulnerabilities, reduce exposed secrets, or detect AI abuse.

Best Practice: Use this data to inform retrospectives and roadmap planning. Incorporate metrics into leadership reporting and prioritize AI security maturity alongside other engineering KPIs. Agentic AI turns static dashboards into dynamic action plans by aggregating and interpreting DevSecOps metrics, identifying trends in code security lapses, and suggesting improvement areas.

Tips: KPIs are early warning signals for emerging problems. Imagine noticing that the average time to detect model drift is getting longer each sprint, or that a spike in revoked secrets hints at rushed, insecure deployments. These aren’t just stats; they’re stories. Treat metrics like these as narrative feedback from your system. Regularly reviewing them with your team during retrospectives can turn lagging indicators into leading ones. The more thoughtfully you track and question what your KPIs are telling you, the faster you can adapt your tools, processes, and risk posture to stay ahead of evolving AI threats.

Goal: Build a continuously adaptive DevSecOps practice that evolves alongside your AI systems. By making improvement an ongoing process, not a one-time effort, teams can stay ahead of emerging threats, improve tooling, and refine workflows to match the rapid pace of AI innovation.

Final Thoughts

As AI reshapes how we build and ship software, the role of DevSecOps is no longer limited to guarding the gates. It’s about being one step ahead and spotting patterns before they become problems, and building systems resilient enough to learn and adapt on their own. Reactive security isn’t enough when AI systems can change overnight. What’s needed is a living, learning security posture that adapts just as quickly as the models and threats it’s designed to protect against.

For DevOps engineers, the grand takeaway is this: you are not just builders of infrastructure and pipelines. You are architects of trust. Embracing AI in your DevSecOps strategy means owning both the power and responsibility of securing tomorrow’s software. The future doesn’t wait, and neither should your security posture.

Visit Checkmarx DevSecOps Solutions to explore how modern tools and practices can help your team thrive.