The AI Agent Risk Checklist for DevOps Teams

DevOps teams are at the forefront of AI adoption. Coding assistants, automated testing agents, deployment bots, and infrastructure automation powered by AI are becoming standard tools. But with great automation comes great responsibility – each AI agent represents a machine identity that requires proper security governance.

This checklist provides DevOps teams with practical, actionable security controls for AI agent deployments. These aren't theoretical best practices; they're real-world controls that balance security with the velocity DevOps demands.

Identity and Authentication

✅ Each AI Agent Has a Unique Identity

Never share credentials between AI agents or between agents and humans. Each agent should have its own service account or identity that enables:

• Precise access control
• Clear attribution in logs
• Independent credential rotation
• Quick revocation if compromised

Anti-pattern: Using a team's shared API key for multiple AI integrations makes it impossible to track which agent did what, and forces you to rotate credentials for all agents if one is compromised.

✅ Credentials Are Stored Securely

AI agent credentials should live in a secrets manager, not in:

• Source code or configuration files
• Environment variables in plain text
• CI/CD pipeline configurations
• Developer laptops or notes

Use HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or similar. AI agents should retrieve credentials at runtime, never have them baked in.

✅ Credentials Are Short-Lived

Long-lived API keys are ticking time bombs. Implement:

• Ephemeral credentials: Tokens that expire within hours, not months
• Just-in-time access: Credentials issued only when needed for a specific task
• Automatic rotation: If long-lived credentials are unavoidable, rotate them frequently

Goal: If a credential is stolen, it should be useless within hours, not exploitable for years.

✅ Authentication Is Strong

Where possible, use certificate-based or token-based authentication rather than simple API keys. Consider:

• OIDC federation for cloud access (no static keys required)
• mTLS for service-to-service communication
• Short-lived JWT tokens with specific scopes

Access Control and Permissions

✅ Permissions Follow Least Privilege

Start with zero access and add only what's needed. For each AI agent, document:

• What resources does it legitimately need to access?
• What operations does it need to perform?
• Why does it need these specific permissions?

Common mistake: Granting broad permissions "to make things work" and never right-sizing them. An AI coding assistant that only needs to read repository files shouldn't have push access.

✅ Access Is Scoped to Specific Resources

Don't grant access to "all S3 buckets" when the agent only needs "this one bucket." Specificity reduces blast radius:

// ❌ Too broad
{
  "Effect": "Allow",
  "Action": "s3:*",
  "Resource": "*"
}

// ✅ Appropriately scoped
{
  "Effect": "Allow",
  "Action": ["s3:GetObject"],
  "Resource": "arn:aws:s3:::ml-training-data/*"
}

✅ Production Access Is Restricted

AI agents for development and testing should not have production access. Separate environments should mean separate identities with separate permissions.

For agents that must touch production:

• Require additional approval gates
• Implement tighter monitoring
• Consider human-in-the-loop for destructive operations

✅ Privilege Escalation Is Blocked

Ensure AI agents cannot grant themselves additional permissions or assume roles beyond their intended scope. Review IAM policies for patterns that could enable privilege escalation.

Monitoring and Detection

✅ All Agent Activity Is Logged

Every action an AI agent takes should produce an audit log entry. This includes:

• API calls and their parameters
• Data accessed or modified
• Authentication events
• Errors and failures

Logs should be centralized, retained appropriately, and protected from tampering.

✅ Baseline Behavior Is Established

For each AI agent, understand what "normal" looks like:

• What resources does it typically access?
• What operations does it usually perform?
• What times of day is it active?
• What's the typical volume of activity?

This baseline enables anomaly detection.

✅ Anomalies Generate Alerts

Configure monitoring to alert on:

• Agent accessing resources outside normal scope
• Unusual volume of activity (potential data exfiltration)
• Activity at unexpected times
• Failed authentication attempts
• Permission-denied errors (may indicate probing)

Integrate with your SIEM or security monitoring platform.

✅ There's a Response Plan

When an alert fires, what happens? Define incident response procedures for AI agent security events:

• Who gets notified?
• How do you quickly disable a compromised agent?
• How do you preserve evidence for investigation?
• What's the escalation path?

Secrets and Credential Hygiene

✅ No Secrets in Code

Implement pre-commit hooks and CI/CD scanning to catch secrets before they reach the repository. Tools like GitGuardian, truffleHog, and detect-secrets can identify credential patterns.

This is especially important for AI coding assistants – research shows repositories using these tools have higher incidence of secret leaks.

✅ No Secrets in Logs

Ensure AI agents don't log sensitive information:

• API keys or tokens
• Authentication headers
• Sensitive data from requests/responses
• Connection strings with embedded credentials

Review logging configurations and sanitize sensitive fields.

✅ Secrets Are Rotated Regularly

Define rotation schedules based on risk:

• High-risk credentials (production access): Monthly or more frequently
• Standard credentials: Quarterly
• Low-risk credentials: Semi-annually at minimum

Automate rotation to ensure it actually happens.

Lifecycle Management

✅ Agent Identities Have Owners

Every AI agent identity should have a designated human owner responsible for:

• Ensuring permissions remain appropriate
• Rotating credentials on schedule
• Decommissioning when no longer needed
• Responding to security events

If the owner leaves the organization, ownership must transfer.

✅ Purpose Is Documented

For each AI agent, document:

• What is this agent for?
• What systems does it interact with?
• What data does it access?
• What's the business justification?

This documentation enables access reviews and helps identify orphaned agents.

✅ Regular Access Reviews Occur

Periodically (quarterly at minimum) review each AI agent:

• Is it still needed?
• Are permissions still appropriate?
• Is the owner still correct?
• Has anything changed that warrants adjustment?

✅ Decommissioning Is Prompt

When an AI agent is no longer needed:

• Immediately disable or delete the identity
• Revoke all credentials
• Remove from any access groups
• Document the decommissioning

Don't let unused agent identities linger with active credentials.

DevOps Pipeline Security

✅ CI/CD Uses Ephemeral Credentials

Pipelines that deploy infrastructure or applications should use:

• OIDC federation to assume cloud roles (no static keys in CI/CD)
• Short-lived tokens scoped to specific deployments
• Just-in-time access that expires after the pipeline completes

✅ Pipeline Permissions Are Segmented

Different pipeline stages should have different permissions:

• Build stage: Read repository, write to artifact storage
• Test stage: Deploy to test environment only
• Production deploy: Separate, more restricted credentials

A compromised test job shouldn't be able to touch production.

✅ Pipeline Configurations Are Reviewed

Treat pipeline configurations as code that requires review. Changes to CI/CD configurations that add new secrets or permissions should require security review.

✅ Third-Party Actions Are Vetted

GitHub Actions, GitLab CI templates, and other third-party pipeline components can be attack vectors. Vet third-party actions for security, pin to specific versions, and monitor for updates.

Third-Party AI Services

✅ Data Sent to AI Services Is Classified

Understand what data your AI agents send to external AI services:

• Is PII being sent to LLM APIs?
• Are trade secrets included in prompts?
• Is the data being used to train models?

Implement controls to prevent sensitive data from reaching inappropriate services.

✅ Third-Party Permissions Are Minimized

When connecting third-party AI tools to your systems:

• Grant minimum necessary permissions
• Use read-only access where possible
• Scope to specific resources rather than broad access

✅ Third-Party Agreements Are Reviewed

Ensure contracts and terms of service for AI services align with your security and compliance requirements:

• Data handling and retention
• Security certifications
• Incident notification procedures
• Right to audit

Summary: Making Security Practical

This checklist isn't about slowing DevOps down – it's about building security into the velocity you already have. The key is automation:

• Automate credential rotation so it happens without human intervention
• Automate secret scanning so it catches issues before they merge
• Automate monitoring so anomalies surface without manual review
• Automate access reviews with tooling that tracks owner attestations

Security that requires manual effort tends to get skipped under deadline pressure. Security that's built into the pipeline happens every time.

Treat your AI agents as you would any powerful team member: verify their identity, give them only the access they need, monitor their work, and maintain accountability for their actions. That's the path to scaling AI safely in DevOps.