Your readiness report Team plan
Remediation roadmap
Your findings, sequenced into a phased plan.
- Agent tool access exceeds least privilege OWASP LLM06: Excessive Agency
- Prompt-injection defenses are only partial OWASP LLM01: Prompt Injection
- No named owner accountable for AI risk NIST AI RMF — Govern 2.1 (roles & accountability)
- Weak defenses against agent goal hijack OWASP Agentic AI — ASI01 Agent Goal Hijack
- Model output isn't fully validated before use OWASP LLM05: Improper Output Handling
- Limited monitoring/logging of agent decisions ISO/IEC 42001 — Clause 9.1 (monitoring & measurement)
- AI incident-response plan is immature NIST AI RMF — Manage 4.1 (incident response & recovery)
- Agentic supply chain (tools / skills / MCP) not vetted OWASP Agentic AI — ASI04 Agentic Supply Chain
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Insights
This customer-facing support agent has a solid governance foundation but carries elevated risk at the LLM and tool layer. The most urgent gaps are excessive tool agency and only-partial prompt-injection defenses — both high-impact for a tool-using agent that handles confidential data. Governance is largely in place; the priority is hardening the agent's runtime controls, then closing the monitoring and incident-response gaps.
Your remediation checklist
Fix: Designate a named, accountable owner for the agent's AI risk so decisions and escalations have a clear home.
Why: named AI risk owner: MISSING
Fix: Build and test an AI/agent incident-response runbook covering detection, containment, rollback, and communication.
Why: AI incident-response readiness: 2/5
Fix: Scope each tool to the minimum permissions, gate high-impact or irreversible actions behind human approval, and add spend/rate caps so a manipulated model can't act broadly.
Why: least-privilege tool scope (excessive agency): 2/5
Fix: Isolate untrusted content from instructions, validate inputs and outputs, and constrain what the model can trigger. Red-team with injection payloads before launch.
Why: prompt-injection defenses: 3/5
Fix: Sanitize and validate model output before it reaches a browser, shell, database, or downstream tool to prevent XSS / SSRF / injection.
Why: model-output validation/sanitization: 3/5
Fix: Log agent decisions and tool calls with enough context to investigate, and alert on anomalies and guardrail trips.
Why: monitoring/logging of agent decisions: 3/5
Fix: Separate trusted goals/instructions from untrusted user and tool-result content, and apply prompt-injection defenses to every input the agent's plan consumes.
Why: agent goal-hijack / plan-injection defenses: 2/5
Fix: Pin and verify tools, skills, and MCP servers to immutable hashes with provenance tracking, and vet upstream trust before a component is loaded.
Why: agentic supply-chain vetting (tools / skills / MCP): 2/5
This AI-generated readiness assessment is for guidance only and is not a certification, audit, or penetration test. Recommendations are grounded in the NIST AI RMF, OWASP LLM Top 10, and ISO/IEC 42001 control text; verify applicability to your environment before acting. Findings assessed: 8.