AI Defense
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Indirect Prompt Injection Explained: Defenses That Hold
How indirect prompt injection works, why LLMs cannot separate instructions from data, and the layered defenses Microsoft and Google use.
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LLM Guardrail Benchmarks: Build Your Own Eval Set
Why published guardrail benchmarks do not transfer, and how to build a held-out eval set with hard negatives, attack success rate, and a latency budget.
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LLM Audit Logging: What to Log, Redact, and Retain
A field-level guide to LLM audit logging: which attributes to persist, what to redact before storage, retention tiers, and the compliance floor.
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Prompt Injection Detection Methods: A Practitioner's Guide
A comparative guide to prompt injection detection methods: classifier scanning, representation analysis, canary tokens, and output monitoring.
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Monitoring LLM Outputs in Production: Anomalies and Drift
How to build production observability for LLM outputs, covering anomaly detection, latency alerting, output drift signals, and sane alert routing.
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Output Filtering Architecture for Production LLMs
How to architect a multi-layer output filtering pipeline for production LLMs: deterministic guards, classifiers, schema validation, and async sequencing.
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Prompt Injection Prevention: Defense in Depth
A systems-level guide to preventing prompt injection in production LLMs, covering layered controls, structural prompt design, and privilege separation.
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AI Defense Techniques for LLMs: A Practitioner's Guide
A technical breakdown of LLM defense techniques, from input guardrails and prompt hardening to dual-model architectures, mapped to OWASP and NIST.