Agent Security Control Plane

Focused security reviews for production AI agents

Agent Security Audit focuses on the operational risks teams hit after the demo: prompt injection, tool permissions, data exfiltration, RAG trust boundaries, audit logs, approval gates, and launch blockers.

Tool boundaryRead, write, send, delete, approve Attack surfacePrompt injection, RAG, tool-chain risk Evidence trailTraces, logs, test results, roadmap Launch gateBlockers, approvals, rollback

AI Agent Security

Prompt injection, MCP, data leakage, tool permissions, and production incidents.

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Readiness Self-Assessment

A fast first-pass check for teams preparing an agent for real users.

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Sample Audit Report

See the kind of evidence, findings, and roadmap a formal review should produce.

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Trust note: We never ask for passwords, verification codes, card numbers, private keys, or software installation. Privacy Terms

AI Agent Security, Guardrails, and Readiness Guides

Agent Security Audit helps teams make AI agents, RAG systems, and tool-using LLM applications safer before they reach real users. The focus is practical: readiness checks, prompt injection testing, framework selection, guardrail review, observability, cost control, and production launch risk.

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Core resource hubs

Foundational guides

Common problems we cover

  • Security: prompt injection, data leakage, unsafe tool permissions, secrets handling, and sandbox boundaries.
  • Reliability: task success, regression testing, RAG grounding, citation quality, fallback behavior, and incident response.
  • Operations: trace review, cost control, latency, human approval gates, audit logs, and release readiness.
  • Tool choice: framework selection, vendor comparison, open-source tradeoffs, and when an agent is the wrong tool.

Recommended paths

Recent field notes

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Contact

For audit requests, partnership notes, or a question about an AI agent launch, email support@ibbs.ai.

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