Tools and credentials
Inventory every tool, API, MCP server, identity and credential the agent can use.
AI Agent Security · MCP · production controls
Secure AI agents before they receive tools or production access.
For CTOs, platform leads and security owners moving an agent beyond a demo. We review what it can reach, what it can change, who approves sensitive actions and what evidence exists when something goes wrong.
Tool permissions · MCP trust · data boundaries · approval gates · audit logs · kill switch · incident ownership
The buyer problem
The agent works. The operating controls around it do not.
The review turns an unclear production risk into explicit access, approval and ownership decisions.
Autonomy boundaries
Separate read, draft, propose and execute actions and define where human approval is mandatory.
Logs and replay
Define the records needed to explain inputs, retrieved context, tool calls, approvals and outcomes.
Trust boundaries
Map sensitive data paths, retention, third-party processors and cross-environment movement.
Misuse scenarios
Test prompt injection, over-broad tools, poisoned sources and unsafe action sequences.
Incident control
Assign detection, escalation, access revocation, kill-switch and recovery ownership.
Decision package
A production decision, not a generic AI policy.
Each finding is connected to an owner, affected component and remediation decision.
- Agent, tool, identity and MCP server inventory.
- Tool-access matrix with read, write and approval boundaries.
- Architecture and data trust-boundary map.
- Prioritized misuse and failure scenarios.
- Logging, replay and evidence requirements.
- Kill-switch, escalation and incident-response design.
- Remediation backlog and go, limit or stop recommendation.
How the review works
Four bounded workstreams turn the agent into a reviewable system.
Map
Document agents, models, data sources, tools, MCP servers, identities, environments and owners.
Challenge
Walk through misuse, failure, prompt-injection and privilege-escalation scenarios against the actual design.
Control
Define least-privilege access, approvals, evidence, monitoring, fallback and incident handling.
Decide
Prioritize remediation and make an explicit go, limited rollout or stop decision.
Good fit
The agent needs real tools, data or production reach.
- An internal pilot is moving toward production.
- MCP servers or custom tools cross trust boundaries.
- The team cannot explain approval, logging or incident ownership.
- Security needs evidence before authorizing rollout.
Not a fit
You only need a generic AI policy.
This review requires a concrete agent architecture, intended users and named tools or data paths. It is not a certification, penetration test or guarantee that an AI system is secure.
Need the AI platform and RAG pipeline first? Review AI infrastructure →
AI Agent Security FAQ
Is this a penetration test?
No. It is an architecture and production-readiness review focused on permissions, tools, data paths, approvals, evidence and operating controls.
Does it cover MCP servers?
Yes. We map each server, its operator, exposed tools, credentials, data access and the trust boundary between the agent and server.
What is the final decision?
The review ends with a prioritized backlog and an explicit recommendation to proceed, limit the rollout or stop until named controls are complete.
Know what the agent can do before production finds out for you.
Bring the architecture, tool list and intended workflow. We will recommend the smallest useful review scope.