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AI Agent Security Assessment

Before your AI agent touches production APIs, know its blast radius.

A focused assessment for teams preparing autonomous or semi-autonomous AI agents for real application, data, and workflow access.

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Best fit

Engineering, security, product, and AI platform leaders preparing agentic systems for production or customer review.

Use this before granting write permissions, production API access, privileged workflow execution, or customer-facing autonomy.

Review focus

TKOResearch maps the agent control plane, tool permissions, data paths, failure modes, and operator controls so your team can make a defensible launch decision.

What you get

Decision support for connected AI systems.

Within 10 business days, you receive: threat model, agent/tool attack-path map, RAG isolation findings, MCP/API permission matrix, sanitized transcripts where applicable, prioritized mitigations, and an executive Go/No-Go memo.

Common assessment scopes

Not every AI-security review needs the same shape. The right scope depends on what the agent can see, what it can do, what can influence it, and what decision the assessment needs to support.

These are common review paths inside a TKOResearch AI Agent Security Assessment.

AI-assisted CI/CD workflow review

For agents, copilots, or LLM workflows that touch source code, pull requests, issues, build pipelines, test output, deployment logic, or remediation workflows.

Review focus

  • Repository permissions
  • Workflow triggers
  • Fork, issue, comment, and pull-request trust boundaries
  • Secrets and token exposure
  • Branch protection and approval logic
  • Agent-generated code, commands, and config changes
  • Tool calls that influence build, test, release, or deployment behavior
  • Artifacts needed to reconstruct what happened after a bad change or manipulated workflow

Use this before allowing an AI system to write code, open pull requests, approve changes, trigger workflows, or influence software delivery decisions.

MCP and tool-use blast-radius review

For agents connected to MCP servers, internal tools, SaaS platforms, local resources, filesystems, APIs, databases, ticketing systems, CRM, email, or other action-capable integrations.

Review focus

  • MCP clients, servers, tools, and credentials
  • OAuth and authorization boundaries
  • Tool descriptions and parameter schemas
  • Read, write, delete, send, publish, approve, deploy, and execute permissions
  • High-impact action gates
  • Local-server exposure
  • Tool-output poisoning
  • Logging, traceability, and kill-switch paths

Use this before connecting an agent to tools or credentials that can change real systems.

RAG and retrieval-boundary review

For agents or LLM applications that retrieve documents, tickets, web content, customer data, internal knowledge, source code, or other context at runtime.

Review focus

  • Tenant and authorization boundaries
  • Retrieved-content trust labels
  • Prompt assembly
  • Instruction/data separation
  • RAG poisoning and stored prompt injection
  • Sensitive-data leakage paths
  • Document-source provenance
  • Context filtering and policy enforcement
  • Artifact capture for retrieved chunks and generated output

Use this before relying on retrieval-augmented generation for customer-facing, internal, regulated, or decision-support workflows.

Production-readiness Go/No-Go review

For teams preparing an AI agent for production, enterprise security review, customer diligence, board review, or broader internal access.

Review focus

  • System boundary
  • Data access
  • Tool permissions
  • Credential scope
  • Operator controls
  • Human approval gates
  • Abuse-case matrix
  • Deployment risk
  • Monitoring and auditability
  • Launch blockers versus acceptable residual risk

Use this when leadership needs a decision-ready answer: ready for production, pilot-only, or blocked pending specific controls.

The assessment output stays practical: trust-boundary map, abuse-case matrix, findings register, prioritized remediation roadmap, and an executive Go/No-Go memo.

AgentBoundary CertifiedMCP checklistScope an assessment

Sample assessment package

Every engagement produces a decision-ready assessment package: an executive Go/No-Go memo, technical findings register, architecture/trust-boundary review, abuse-case matrix, and prioritized remediation roadmap.

Go/No-Go Launch Memo

Decision-ready memo stating whether the agent is ready for production, pilot-only, or blocked pending specific controls.

Agent Trust-Boundary Map

Diagram of model, memory, RAG, tools, APIs, credentials, logs, and approval gates across the deployed workflow.

Abuse-Case Matrix

Structured tests for prompt injection, indirect injection, RAG leakage, unsafe tool use, and excessive autonomy.

Findings Register

Severity-ranked findings with artifacts, business impact, recommended fix, owner, and validation method.

Remediation Roadmap

Prioritized engineering plan separating pre-launch blockers, short-term controls, and post-launch hardening.

Risk addressed

Tool, data, and trust-boundary risk in one review.

  • Covers AI agents with real tool access, not static chatbot demos
  • Covers auth boundaries, tool permissions, retrieval paths, and operator controls together
  • Outputs are written for engineering action and executive decision support

When to use it

Principal-led, architecture-level review for high-stakes agent deployments.

Use this when a specific system boundary needs senior technical judgment before production access, customer review, or executive approval.

Written scope

Request a scoped review before granting broader system access.

Request written scope