$670K
SHADOW AI BREACH PREMIUM
Hidden or unapproved AI agents can make breaches far more expensive. TrustableClaw helps uncover rogue agent activity before it turns into a costly incident.
TrustableClaw makes AI agents compliant, safe and controlled. Its Deterministic AI detects when agents can betray users, then protects every important action with permissions, approvals, tamper-evident receipts, adaptive memory and built-in evidence for SOC 2, HIPAA, ISO 27001, GDPR, the EU AI Act, NIST AI RMF and more
$670K
Hidden or unapproved AI agents can make breaches far more expensive. TrustableClaw helps uncover rogue agent activity before it turns into a costly incident.
$4.4M
When AI agents touch sensitive systems or data, you need proof of every important action. TrustableClaw creates tamper-evident receipts and clear audit trails.
$50K–$150K
Audit prep gets expensive when evidence is scattered and manual. TrustableClaw generates audit-ready records automatically as agents work.
€30M
New AI rules require accountability, controls, and evidence. TrustableClaw helps map agent activity to governance requirements and regulator-ready documentation.
TrustableClaw reduces this risk with agent discovery, approval controls, tamper-evident receipts, and audit-ready evidence.

TrustableClaw reports 100% auditability coverage across OpenAI's full HumanEval benchmark
Download TrustableClaw and start using a governed AI agent with approvals, receipts, audit trails, compliance workflows, trust scoring, and adaptive memory.
When an AI agent makes a mistake, most teams can't answer the basics: what happened, why it happened and who approved it. If you can't answer those questions, you don't control your AI.
TrustableClaw gives teams proof that cannot be disputed. Every important AI action is captured, every sensitive decision requires human approval and every interaction produces a signed, verifiable receipt.
The Enterprise AI Agent Scanner goes further, detecting unsafe patterns in third-party and external agents, even systems your team didn't build and can't see inside.
TrustableClaw is self-learning, getting smarter, more accurate and more cost-efficient with every approval, correction and decision your team makes.
The result: AI your team can control, audit, improve and trust, that gets smarter and cheaper to run every single day.
Supports: SOC 2 · HIPAA · ISO/IEC 27001 · EU AI Act · GDPR · NIST AI RMF · NIST CSF 2.0 · ISO/IEC 42001 · PCI DSS · FedRAMP · HITRUST CSF · FDA AI/ML SaMD

The more it runs, the more it knows. The larger the organization, the more powerful it becomes.
50K-Node
Built for individuals and local workflows. One person's AI gets smarter over time through governed interactions and adaptive memory.
1M+ Node
Built for organizations running AI agents. The more your team uses it, the smarter and more reliable it becomes across every department.
Contact Us for Enterprise pricing.
Let AI do useful work while keeping humans in control. Sensitive actions can require approval, be blocked by policy, or be recorded with proof.
→ The AI knows when to stop and ask
Create proof records for important AI actions, including what happened, when it happened, and the hashes needed to verify it later.
→ Every important AI action can leave a receipt
Record important AI events in a hash-linked ledger so actions, approvals, and results can be reviewed, exported, and checked later.
→ Not just logs. Evidence
Ungoverned AI agents carry a price tag. TrustableClaw lowers it.
One prevented breach covers years of TrustableClaw licensing.
Verify receipts, inspect proof records, export proof packages, and review past AI runs when something needs to be trusted later.
→ AI work becomes reviewable
Collect evidence, review gaps, manage exceptions, and export proof-ready packages for SOC 2, HIPAA, ISO 27001, EU AI Act, NIST AI RMF, GDPR, PCI DSS, FedRAMP, and more.
→ Built for teams that need AI work to be reviewable
Store useful facts, corrections, outcomes, and evidence in an auditable memory graph instead of relying only on the current chat window.
→ The agent learns from experience without changing model weights