AI Agent & MCPSecurity Services forProduction Systems

We help teams make AI agents safer to run in production by identifying where they can be abused, putting the right safeguards in place, and validating them through hands-on security testing.

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Do you know what your AI agents can access right now?

9 seconds to wipe production

A Cursor agent running Claude Opus 4.6 used an over-scoped Railway token to delete PocketOS's production database and volume-level backups in a single API call.

The Register

“Do not touch production” wasn't enough

Replit acknowledged an incident where its Agent deleted customer data because development activity could affect the production database. Replit then introduced default development and production database isolation.

Replit

Practical engagements for securing agentic infrastructure

Agent Security Assessment

Identify security risks across AI agents, MCP servers, tools, permissions, credentials and data flows.

Deliverable

Architecture review, attack-path analysis and a prioritized remediation plan.

Start with an assessment

Agent Security Architecture

Design a secure architecture for AI agents, MCP infrastructure and controlled tool execution.

Deliverable

Trust boundaries, Security Gateway blueprint and a practical control model.

Design the control model

Agent Security Implementation

Implement policy enforcement, isolation and monitoring across agentic AI infrastructure.

Deliverable

Runtime controls, scoped access, approvals and production-grade observability.

Build the controls

Agentic AI Red Teaming

Test AI agents and MCP infrastructure against realistic attack paths and tool-abuse scenarios.

Deliverable

Attack simulations, proof-of-concept findings and remediation validation.

Test your defenses

Agentic infrastructure needs a security layer

Agents make dynamic tool decisions, process untrusted content and can operate with machine credentials. A compromised or manipulated agent can become an execution path into the systems it can reach.

AI security gateway connecting agents to protected infrastructure

Use Cases for Agent Security

Putting AI agents into productionExposing internal tools through MCPGiving agents cloud accessConnecting agents to SaaS appsHandling credentials inside agent workflowsAllowing agents to execute production actions
Deploying autonomous coding agentsIntroducing multi-agent workflowsTesting existing agent security controlsAdding approval flows for sensitive actionsEnforcing least privilege for agents
Preparing for enterprise security reviewsSecuring third-party MCP serversAuditing agent actions and tool callsRed teaming agentic systemsBuilding security controls around AI automation

The questions teams ask before they centralize control

AI agent security is the practice of protecting AI agents, the identities and credentials they use, the tools they can invoke, and the systems they can access. It includes agent authentication and authorization, least-privilege access, MCP and tool security, secrets management, runtime policy enforcement, human approval for sensitive actions, agent-to-agent security, and audit logging. The goal is to keep agentic AI systems within defined security and execution boundaries even when an agent makes an incorrect decision or processes malicious content.

Secure your agent infrastructure before it becomes a production attack path

Get a focused review of your AI agent, MCP and tool-access architecture — and a prioritized plan for centralized security controls.