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Executive Summary

Enterprises running autonomous AI agents increasingly lack reliable, runtime auditability and accountable evidence for agent-driven decisions, a gap that is especially acute in regulated sectors such as finance, healthcare, energy and large digital platforms where legal and operational risk is material; there are roughly 500,000 enterprises that could need an AI governance and agent security suite, implying a $15.0B TAM at $30K ACV. You could build an "adversarial witness" that runs alongside deployed agents to continuously generate adversarial probes and automated critiques while recording immutable provenance—inputs, prompt and API call history, model versions, chain-of-thought snapshots and cryptographic attestations—for use in audits and incident response. The system would produce packaged evidence for regulators, a configurable policy and risk-scoring engine, and turnkey integrations with SIEM, GRC and procurement workflows to make the outputs actionable for security, legal and compliance teams. This is an attractive moment because agentic-AI adoption is multiplying the number of autonomous decision-makers in production, regulation and procurement increasingly demand provenance and explainability, and enterprises are shifting budget from model-centric observability to decision- and audit-centric tooling; these trends create urgent, high-value spend. With a $15B TAM and estimated $30K ACV, deals can be sticky—compliance and audit capabilities are expensive to replace once embedded—but you must move quickly to capture early enterprise pilots. To stand out you should emphasize active, adversarial critique (not just passive logging), tamper-evident cryptographic provenance, and deep enterprise integrations and evidence packaging tailored to auditors—differentiators versus observability and static governance vendors. Be honest about the challenges: engineering low-latency, low-overhead probes, managing false positives, and integrating across heterogeneous agent runtimes and cloud environments will require strong engineering resources and sales access to early customers before scaling.

Executive Summary

Auditable, adversarial witness for deployed AI agents — automated critique & provenance

Executive Summary

Auditable, adversarial witness for deployed AI agents — automated critique & provenance

Executive Summary

Market Validation

Market Opportunity

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