AI Attack Surface & Threat Modeling

AI systems expand your attack surface: new inputs, new tool calls, new data paths, new failure modes. Many teams ship fast but lack a structured security model, leading to leakage risk, unauthorized actions, and brittle controls.

We run a practical AI threat-modeling process:

• Map trust boundaries, tool permissions, and data flows
• Identify abuse paths (prompt injection, data exfiltration, privilege escalation)
• Define concrete controls (allowlists, least privilege, validation, monitoring)
• Produce an audit-ready security plan with prioritized fixes

• Threat model and security requirements for critical flows
• Tool authorization layer (who can do what, under which conditions)
• Data boundary controls (redaction, minimization, retention)
• Logging that supports incident response without leaking sensitive data

• Coverage of critical flows with explicit controls
• Reduced risky tool calls via allowlists and gating
• Clear incident playbooks and measurable alert signals

• Avoid “security by prompt”: enforce controls in code and policy layers
• Assume hostile inputs: validate and sanitize consistently
• Keep logs safe: redact PII/secrets and restrict access

If you want a clear picture of your current risk and a prioritized hardening plan, request an AI Security Audit.