Automated AI Red Teaming for Continuous AI Risk Detection

• the model & prompt layer (jailbreaks, system-prompt extraction, indirect injection)
• the API authorization layer (cross-tenant access testing that names the exact record reached)
• the tool & MCP layer (function-calling and Model Context Protocol abuse)
• the agentic workflow layer (privilege escalation, approval bypass, race conditions) across 14 attack families, each mapped to OWASP LLM/API/Agentic Top 10 and MITRE ATLAS.

The engine runs a four-phase process: Recon maps system capabilities and guardrails; Exploit runs multi-turn, multi-modal attack chains across selected threat families; Verify re-runs successful attacks to confirm reproducibility; Judge scores each finding for severity with evidence checked against the actual transcript, eliminating hallucinated findings.

Every finding is delivered as an executive-ready report with clear PASS/FAIL outcomes, reproducible exploit evidence, and mappings to OWASP, MITRE ATLAS, and the regulations governing your AI - EU AI Act, GDPR, India's DPDPA, NIST AI RMF, and ISO/IEC 42001. Ship a fix and re-run the engagement: every finding returns a fixed / still-failing / regressed verdict, so you can prove the gap is actually closed.

Safety is built in, not bolted on - runs are restricted to authorized targets only, testing is non-destructive by design (no destructive tool or database actions), and an always-on SSRF guard prevents misuse.

Typical usage patterns: initial validation (2-5 evaluations), ongoing monitoring (10-20 per quarter), production-scale coverage (40+ annually). Pricing is usage-based, allowing teams to scale testing as needed.

Move from one-time testing to continuous AI security assurance.