Guardrails for enterprise AI, without the theater
How to build AI governance that actually reduces risk — and doesn't just generate a policy binder nobody reads.
AI governance is having its moment, and most of what's being produced is theater — long policy documents nobody reads, sitting alongside production systems nobody has audited.
Real guardrails are technical, testable, and specific. Input filtering for prompt injection. Output filtering for PII leakage. Rate limits and cost caps by user. Audit logs that a compliance team can actually query. Red-team suites run on every model change.
None of this is glamorous. All of it is verifiable. Both properties matter — a guardrail you can't verify is a guardrail you don't have.
Marcus builds production LLM systems. Formerly staff engineer at two AI-first startups and a hyperscaler.
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