Technical writing on AI governance, SOC 2 compliance, and building enterprise-grade agent infrastructure.
High-risk AI enforcement began August 2, 2026. Regulators can now audit a live decision — not just a policy document. Most enterprise AI stacks were not built for that.
Read post →Source and task gating alone can't stop a well-scoped agent from executing a write it shouldn't. Account freezes and credit decisions need their own gate — not just access to the source.
Read post →A gateway routes. It doesn't govern — that was the abstract argument. agentgateway is where we built the concrete version of it first, and it won't be the only one.
Read post →Every enterprise already has an agentic harness — sometimes three of them. Here's why governance still has to be a separate product sitting outside it, not a feature bolted onto it.
Read post →Every agent task passes through six checkpoints before it's done. AutoPIL enforces four of them in real time. Here's the map — and why OpenAI and Anthropic's recent containment failures prove the missing layer can't stay optional.
Read post →"Gateway" means two different things in the agent ecosystem right now — an enterprise API gateway and an AI-native LLM gateway. Neither one decides whether a specific data access should have been allowed. Here's where AutoPIL sits relative to each.
Read post →Most enterprises have lifecycle governance — model cards, approvals, compliance reports. Almost none have runtime enforcement. Here is the gap, why it exists, and why it matters before August 2.
Read post →A practical guide for enterprise teams deploying AutoPIL self-hosted — ECS, RDS, PrivateLink to Databricks Serverless, the architectural decisions that keep enforcement inside your perimeter, and the gotchas that aren't in the documentation.
Read post →Most AI governance tools only see what agents do through the SDK. We built a three-stage model that closes the gaps — design-time static scanning, SDK and gateway guards, and a runtime sidecar that intercepts at the container network layer regardless of framework.
Read post →Agent policy tells you what a role is permitted to do. It doesn't tell you who is actually behind the call. Here's why we added the principal to every audit event and what it changes about how enforcement works in practice.
Read post →A deep look at the five credential types — api_key, jwt_oidc, mtls, spiffe, conjur — how key binding works in both directions, and why the identity_method on every audit event is the difference between a defensible audit trail and a marketing page.
Read post →The industry has a vocabulary problem — and it's hiding a real risk. Here are the four questions every enterprise AI deployment has to answer in writing before agents go to production.
Read post →135 pre-built policies across 12 industries, a tamper-evident audit chain, and integrations for every major framework. What we built, what building it taught us, and what we're opening up ahead of a public launch in May.
Read post →Every AI governance framework focuses on what agents can do. The real risk is what they can see. Context is where the sensitive data lives — and governing it is the only path to genuine trust.
Read post →At five agents, baking governance into each one looks manageable. At fifty, you have fifty different failure modes and no single lever to pull when policy changes.
Read post →When agents hand work to other agents, your governance surface doesn't add — it multiplies. Governing each agent individually is not governance. It's sampling.
Read post →Most teams treat SOC 2 as a paperwork exercise. Here's what CC6.1 and CC6.3 actually demand — and why your API key strategy needs to change before your next audit.
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