SentinelPoT validates consequential agent actions before they execute, red-teams AI systems continuously, and produces tamper-evident evidence for every decision it approves or blocks.
Logs, audits, and incident reviews describe what an AI system already did. SentinelPoT sits in the path of execution and decides whether a consequential action should happen at all.
Every tool call, transaction, or infrastructure change an agent proposes is checked against policy, authorization scope, adversarial tests, and applicable controls. The outcome is recorded as a signed attestation before anything runs.
Observability · post-incident review · quarterly audit · retroactive compliance mapping
Pre-execution validation · scoped tool authorization · human approval gates · attested evidence per action
Three product families share one validation engine and one evidence format, so a finding from red teaming becomes a runtime control, and a runtime decision becomes an audit record.
Pre-execution validation for agent actions, tool calls, workflows, and high-risk decisions. Four proof dimensions run as a single check.
Managed AI red teaming and private researcher programs, purpose-built for LLM, RAG, agentic, multimodal, and ML systems.
Reproducible validation, remediation retesting, cryptographic attestations, and audit-ready records mapped to your control framework.
Each proof dimension is a family of checks that runs in parallel on every consequential action. Which checks fire, and how deeply, is set by the action's blast radius and your policy — not by a fixed pipeline.
Every check that runs is named in the attestation, so the evidence shows not just the verdict but what was examined. All capabilities are delivered through pilot engagements — scoped to your system, configured and operated with the SentinelPoT team, with signed evidence at the end. Request a pilot →
SentinelPoT is a gate, not a dashboard. It receives a proposed action, returns a verdict and an attestation, and the caller executes only on approval.
from sentinel import guard, Policy # Every call is validated before it runs. # Blocked calls raise and are routed to human review. @guard(policy=Policy.load("treasury-v3"), attest=True, approver="finance-ops") def transfer(amount_usd: float, to: str) -> Receipt: return bank.wire(amount_usd, to) # → verdict: APPROVED score: 96 # → attestation: sha256:5b3e…a91c (signed, tamper-evident)
Traditional bounty platforms were designed for web applications. AI systems fail differently: through language, context, memory, and delegated authority. Sentinel Research pairs vetted human researchers with automated adversarial suites, and every finding is reproduced, scored, and retested.
Managed red-team engagements and private, invitation-only programs against defined AI assets. Findings arrive with reproduction steps, severity, and a runtime control recommendation.
Attack suites run against every model or agent release, so regressions surface before deployment rather than in production.
A confirmed injection path or tool-abuse chain becomes a Sentinel Runtime policy. Retesting confirms the fix, and the attestation records it. Research feeds prevention; prevention produces proof.
SentinelPoT is most valuable where an agent's action is hard or impossible to reverse. We start with three domains where that is already true.
Payment agents, treasury automation, trade execution, and customer-facing assistants with account authority.
AI-controlled wallets, autonomous treasury agents, DeFi strategies, oracle and bridge interactions, agent-generated transactions and contract logic.
Agents with write access to databases, internal APIs, CI/CD, and cloud infrastructure across multi-agent workflows.
A pilot starts with an AI assurance assessment: we red-team one agent or model, instrument its highest-risk actions with Sentinel Runtime, and hand you signed evidence of what was tested and what is now enforced.