Pillar Guide
Responsible AI: an engineering definition that holds
Responsible AI decomposed into engineering terms: integrity, security, accountability, and transparency, and the operational controls that make each real.
Topic
AI & AI Integrity
AI integrity is the engineering discipline of making AI systems trustworthy in production: verifiable behavior, governed data paths, security against manipulation, and architectures that keep humans accountable for outcomes. This section covers enterprise AI architecture, AI governance, evaluation, and AI security.
- AI integrity engineering
- Enterprise AI architecture
- AI governance and evaluation
- AI security and threat modeling
- Retrieval and grounding patterns
Start here
The pillar guides — orientation pages that map this territory and point to the deep-dive articles.
Pillar Guide
AI governance: decision rights, evidence, and control
What AI governance actually is: decision rights backed by evidence, the artifact set that makes it real, and where the EU AI Act and NIST AI RMF fit.
Articles & guides
An enterprise AI adoption framework that survives contact
A staged enterprise AI adoption framework from an architect's seat: use-case triage, build vs buy vs wait, platform foundations, and metrics that matter.
AI risk management for engineers, not auditors
AI risk management that engineers can run: a five-domain taxonomy, assessments that produce decisions, NIST AI RMF mapping, and monitoring that closes the loop.
RAG architecture for the enterprise
Enterprise RAG architecture end to end: pipeline design, chunking and index tradeoffs, permission-aware retrieval, and measuring grounding faithfulness.
AI evaluation in production: evals as regression tests
A working AI evaluation program: golden sets that gate releases, drift monitoring, human review sampling, and incident thresholds that trigger a rollback.
LLM security: threats and defensive architecture
A practical LLM security threat model — prompt injection, data exfiltration, tool abuse, supply chain — and the defensive architecture that contains them.
AI governance engineers won't route around
AI governance that ships as code: policy-as-code, model cards, audit trails, and the NIST AI RMF mapped to engineering artifacts your teams already produce.
Enterprise AI architecture patterns that hold up
Five enterprise AI architecture patterns — gateway, retrieval grounding, human-in-the-loop, evals, model portfolio — when each applies, plus the anti-patterns.
What is AI integrity? An engineering framework
AI integrity as an engineering discipline: verifiable behavior, governed data paths, resistance to manipulation, and a maturity model to build against.