Editorial desk
AI Defense Editorial
AI Defense Editorial is the publishing identity for AI Defense. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to hello@aidefense.dev. More detail is on the about page and the editorial disclosure.
Posts (16)
- Defense
Secure RAG Implementation: 7 Controls in Build Order
A build-order checklist for secure RAG implementation: vector store auth, ingestion sanitization, retrieval ACLs, chunk scanning and grounding checks.
- Threats
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.
- Guardrails
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.
- Monitoring
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.
- Detection
Jailbreak Detection for LLMs: Methods and Limits
Jailbreak detection for LLMs combines pre-inference heuristics, trained classifiers, and output checks, but adaptive attacks leave residual risk.
- Guardrails
8 LLM Guardrail Tools and Frameworks Compared (2026)
Compare eight LLM guardrail tools and frameworks by deployment model, prompt-injection defense, PII coverage, latency, licensing, and best fit.
- Defense
Secure RAG Architecture Best Practices for Production
This guide explains secure RAG architecture controls for vector databases, retrieval isolation, access control, monitoring, and corpus governance.
- Detection
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.
- Guardrails
How LLM Guardrails Work: Architecture and Trade-offs
How LLM guardrails work: the six runtime layers, choosing controls per threat, what each layer costs in latency, the tooling, and the failure modes.
- Defense
ML Model Provenance: Signing and Verification Guide
Learn how model provenance, Sigstore signing, SLSA attestations, safe formats, and load-time verification secure the ML supply chain.
- Monitoring
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.
- Defense
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.
- Defense
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.
- Defense
Implementing Rate Limiting and Abuse Detection for AI APIs
An engineering guide to rate limiting, quota enforcement, and abuse detection for AI APIs, covering token buckets and behavioral fingerprinting.
- Defense
Building an Internal Adversarial Testing Pipeline for LLMs
How to build an internal adversarial testing pipeline for LLM applications with garak, promptfoo, and custom probes, including a CI integration pattern.
- Defense
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.