AI Platforms & Models

AI Security & Observability

Before broad rollout, not after. Logs you did not collect cannot be reviewed retrospectively.

Security and observability for AI means authentication per request, authorization at retrieval, output filtering, and immutable logging of who asked what, which model ran, what data was reached, and what followed.

That last field is the one most often missing, and the one auditors ask about, because it connects a system's output to something that happened in the world.

What the work includes

  • Per-request authentication regardless of where the request originates
  • Authorization evaluated at retrieval so it cannot be bypassed via the API
  • Output filtering for policy violations and sensitive data exposure
  • Append-only audit logging with integrity verification
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Secure AI Implementation

Whenever an AI system will touch data that is not already public, which in practice means almost every enterprise deployment.

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AI Governance Framework

Before AI use spreads beyond a pilot team, or after it already has and nobody can say who is using which capability on what data.

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Agent Monitoring & Optimization

Once agents are running unattended, when the question shifts from whether they work to whether they still work.

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Data Governance for AI

Before an AI system is pointed at a corpus nobody has classified, which is the point at which governance stops being theoretical.

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// Common Questions

Questions
About This

Common questions about AI Security & Observability.

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What was asked, what was retrieved, which model answered, what it returned, and who saw it. Together these allow an answer to be reconstructed later, which is what any security review, incident, or regulatory question will require.

Because logs you did not collect cannot be reviewed retrospectively. An incident that predates instrumentation is unreconstructable, and that is exactly when the records are wanted. This is the one part of the work that cannot be added later.

Prompt injection attempts, retrieval of material a user should not have reached, sensitive data appearing in outputs, and drift in behaviour after a model or data change. None are visible from application logs that only record requests and status codes.

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