Artificial intelligence (AI) is becoming a core component of the modern development pipeline. Every industry faces the same key questions about testing and protecting AI systems, and needs to explain complexity, dynamic nature and newly introduced risks. The new OWASP AI Test Guide is a direct response to this challenge.
This community-created guide provides a comprehensive and evolving framework for systematically assessing AI systems across different dimensions, including adversarial robustness, privacy, equity, and governance. Models aren’t the only way to build safe AI. It includes everything surrounding them.
Most of today’s AI workflows rely on nonhuman identity (NHIS). Service accounts, automation bots, temporary containers, CI/CD jobs. These NHIs manage the infrastructure, data movement, and orchestration tasks that AI systems rely on. AI testing becomes controversial when attackers do not pass the model and access is not secured, governed or monitored. They would just avoid it.
Let’s take a look at the fundamental concepts found in the OWASP AI Test Guide and see where their advice matches the Secrets Security and NHI governance goals that many teams are already pursuing.
Look at the dimensions of the OWASP AI test
The OWASP AI Test Guide provides an overview of several core dimensions of AI risk, ranging from security misconceptions to data governance and adversarial resilience. Model-level testing often governs the conversation, but a significant portion of these risks can be traced back to how nonhuman identity and secrets are managed across the system.
Security Test: Secret Exposure and Misunderstanding
Security testing within an AI environment must start with the way secrets are provisioned, stored and exposed. Whether environment variables are protected, whether the CI/CD pipeline is injecting secrets, or whether the infrastructure that acquires the model leaks sensitive access tokens is just as important as the output of the test model.
One of the key objectives of the OWASP AI Test Guide is to ensure that the principles of least privilege and zero trust govern secrets. The goal is that authorities are not allowed to over-supervised components of AI systems. A similar approach is required for privacy and data governance. If training datasets are fed through APIs or repository protected only by embedded credentials, these access paths should be tested as part of the system’s privacy attitude.
Credential leaks allow unauthorized users to access training data and increase the risk of privacy violations and model inversion attacks. Mapping the relationship between NHIS and data access points is essential to understanding whether an AI system truly complies with privacy requirements.
Hostile Robustness: Supply Chain and Agent Integrity
Adversarial robustness is not limited to inputs created to confuse the model. It also includes how external agents and third-party tools are integrated into AI workflows. These components often rely on tokens or secrets for approval. If these credentials are outdated, over-adjusted, or reused across components, an attacker may not need to exploit the model directly. Instead, you might compromise on the plugins or containers surrounding it.
In particular, in a world where “vibe-coded” systems are hit with production, ensuring that these dependencies are tested for secret hygiene is a fundamental security task.
Surveillance and governance
Finally, this new test guide highlights the importance of monitoring and governance. Continuous visualization of how secrets are used, rotated and revoked forms the backbone of enforceable AI security policies. The test should not be stopped at the initial deployment. It must continue as the environment evolves. Warning about the use of nonhuman identity, attempts to access unauthorizedly, and maintaining a historical timeline for the use and exposure of credentials all support a test-driven approach to governance.
The OWASP AI Test Guide calls for a layered approach to security. This not only focuses on models, but also addresses a complete environment of access, automation, and identities that enable them. Secrets and NHI management no longer support concerns. These are central to whether AI systems can be effectively trusted and tested.
The role of Gitguardian in building a policy-driven AI security culture
This latest test guide from OWASP emphasizes that true protection of your AI system means not only applying reactive patches, but also building processes that use continuous infrastructure. For an organization to succeed here, the policy must be enforceable and enforcement must be measurable and effective.
This is where Gitguardian’s NHI-focused approach becomes important.
Insight into NHI inventory
At the heart of Gitguardian’s platform is a unified secret inventory across code repositories, CI/CD pipelines, containers and cloud environments. But visibility is just the beginning. GitGuardian maps each secret of the nonhuman identity (NHI) that uses it, and links the behavior of the infrastructure with access governance. This allows security teams to analyze not only whether secrets exist, but who or what they are using, and whether their use matches the defined policy. For the first time, you can have a unified view of your NHI, no matter what shape they take.
By tracking NHIS and associated permissions, GitGuardian allows organizations to identify overscoop tokens, detect secret reuse in different environments, and verify minimal enforcement. This level of insight supports aggressive testing. Security teams can simulate policy violations, get secret alerts that are hard coded before integration, and continually assess compliance as infrastructure evolves.
Governance + Scale of response automation
Beyond prevention, Gitguardian strengthens incident response and long-term governance. The platform provides real-time alerts with leaked or rotated secrets, integration with SIEM for integration with SIEM and SOAR tools, and timelines of secret incidents that allow for root cause analysis and forensic medicine. This combination of telemetry and traceability brings organizations in line with guide governance and monitoring requirements.
Gitguardians don’t just protect secrets. Change how secrets are governed throughout the AI workflow. Teams allow teams to build policies around their identity, implement them consistently and continuously validate them, ensuring they are as reliable as the AI systems their infrastructure supports.
A practical example: Protect your LLM pipeline with GitGuardian
To explain how this actually works, consider a team of people responsible for fine-tuning your own LLM using internal datasets. Those development workflows include a training repository filled with scripts and configurations, Docker containers deployed via CI/CD, API integrations for querying unique data services, and scheduled retraining agents with persistent infrastructure access.
This type of setup represents the rich and diverse landscape of NHIS, each with its own operational scope, a set of credentials and unique risks. GitGuardian integrates seamlessly across these sources, detects secrets embedded in your code before reaching production, accidentally burns container images of your credentials into your infrastructure, and tracks API tokens as they move between environments. GitGuardian can map these secrets to their respective NHIS.
This mapping allows security teams to ask difficult questions.
Why does this retraining agent have access to production data and staging credentials? Why are developer tokens reused by orchestration services? Did the critical token remain indifferent for six months across multiple pipelines?
In Gitguardian, these questions are no longer theoretical. They are answerable, auditable and practical.
By continuously monitoring secret usage and correlating it with NHI’s behavior, GitGuardian converts what would otherwise be a black box into a transparent system. The AI infrastructure comes in line with OWASP’s expectations for governance, testing and assurance, providing teams with a clear path from vulnerability identification to remediation and policy verification.
From test models to ecosystem security
AI security is not just an adversary example. It’s about building a reliable end-to-end system. Your trust should cover NHIS and secrets fueled by training work, container deployment, and plugin integration. This is clear from the OWASP AI Test Guide and we could not agree any further.
Gitguardian brings this trust to your reach. By building a unified secret inventory and mapping them to the NHIS that use them, Gitguardian allows organizations to implement security policies as a living testable system. Equip your team to validate access control across all layers of your AI infrastructure, reduce identity sprawls, and enforce minimal privileges.
For organizations looking to match this guide or one of the top 10 guides from OWASP, this level of control and visibility is fundamental and not optional. GitGuardian turns hidden risks into viable insights and helps security and ML teams move from patchwork defense to aggressive governance.
Ready to take the next step to ensure your AI workflow? Set up a demonstration with Gitguardian today to see how NHI governance and secret hygiene can increase AI security attitudes from reactive to resilience.
***This is the Gitguardian Blog’s Security Blog Network Syndicate Blog. Consider the Secrets Security controls created by DwayneMcDaniel. Read the original post at https://blog.gitguardian.com/owasp-ai-testing-guide/
