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AI Agent Audit
Your agents are already running in production. Are you certain of what they can access — and what they're leaking? Indext Data Lab delivers a structured security and governance audit of your AI agent infrastructure, identifying data exposure, scope creep, and compliance gaps before regulators or attackers do.
100% Job Success
Top-Rated Plus
Expert-Vetted
100% Job Success
Expert-Vetted
Top-Rated Plus
Top-Rated Plus
100% Job Success
Expert-Vetted
100% Job Success
Expert-Vetted
Top-Rated Plus
- 85%of enterprises deploy agents without a formal access review
- 72htypical audit delivery from kickoff to final report
- SOC 2GDPR · HIPAA · ISO 27001 compliance mapped
The risks hiding in your agent stack
AI agents reason, plan, and execute autonomously — often with access rights no one explicitly approved. Traditional security tools see the permissions, not the behavior. We look at both.
Identity ambiguity
Agents deployed on shared service accounts or inherited API keys. When something acts, no one can attribute it — a compliance and forensics failure waiting to happen.
Each incremental change to an agent's access seems reasonable in isolation. Months later it holds capabilities no one explicitly approved and no policy document reflects.
Permission drift
Shadow AI connections
MCP servers, third-party APIs, and toolchains connected without a security review. Every new integration extends your attack surface — silently.
Logs show an action occurred but not why the agent took it. Without ADRs and reconstructable reasoning chains, you cannot answer regulators six months later.
Decision opacity
Data exfiltration paths
Prompt injection, RAG poisoning, and citation manipulation can route sensitive data through an agent to an external endpoint — without triggering a single traditional DLP alert.
Agentic systems can acquire new capabilities mid-task. An agent assigned to summarise documents can end up with write access to production databases.
Scope expansion
What you receive
- Agent inventory reportFull registry of deployed agents, identities, toolchains, and access scopes
- Risk findings documentPrioritized vulnerabilities mapped to OWASP, MITRE ATLAS, and your compliance framework
- Audit trail templateLogging architecture to produce defensible, regulator-ready records of all agent activity going forward
- Remediation playbookSprint-ready engineering tasks with acceptance criteria — implementable by your team or ours
How we run the audit
Inventory and discovery
We map every deployed agent, MCP server, API key, and toolchain in your environment — including shadow deployments not visible in your central registry.
Identity and authorization review
We verify each agent operates under a distinct, traceable identity. We check that permissions are intentionally scoped, not inherited or accumulated over time.
Behavioral analysis
We reconstruct agent decision chains — inputs, intermediate tool calls, and outputs — to identify actions that exceed the agent's stated purpose or user intent.
Supply chain assessment
We evaluate every external tool, third-party service, and LLM API in your agent's dependency graph — rating security posture and exposure at each node.
Compliance gap mapping
We map findings to OWASP Top 10 for LLMs, MITRE ATLAS, SOC 2, GDPR Article 22, and HIPAA — giving your legal and compliance team an actionable evidence package.
Remediation roadmap
Every finding is prioritized by exploitability and business impact. We provide concrete engineering-level fixes — not just a risk register.
FAQ: Common Questions
An AI agent security audit evaluates how your agents handle data, permissions, and external tools. It helps identify risks like data leakage, prompt injection, and uncontrolled actions in production systems.
An LLM agent security review covers prompt injection testing, data exfiltration risks, unsafe tool usage, and identity management. We simulate real-world attacks to assess how your agents behave under stress.
Yes. Our AI agent compliance audit ensures your system aligns with GDPR, SOC 2, and internal governance policies, identifying gaps in data handling, access control, and auditability.
An agentic AI audit focuses on autonomous behavior — how agents make decisions, call APIs, and expand scope over time — rather than just static infrastructure or code security.
An AI agent governance audit ensures agents operate within defined boundaries, with proper access control, identity management, and logging. It’s essential for enterprises and startups scaling agent-based systems.