Turn AI-assisted engineering into a reviewable delivery contract. Define requirements, interfaces, authorization, failure behavior, testing, operations, and evidence before claiming an implementation is complete.
Assess enterprise AI through a traceable chain of system boundaries, threats, controls, evidence, residual risk, and approval. Distinguish proposed controls from tested behavior and reassess material changes.
Govern dynamic model routing without allowing fallback to inherit production authority. Record resolved models, qualify route members by action, and define when degraded paths must require approval or hold execution.
Restore AI agent data under a current recovery authority. Reconcile approvals, claims, credentials, and target effects before admitting bounded successor execution.
Move from an offline simulation to a disposable Kubernetes cluster. Test separate agent, executor, and observer identities, resource-scoped permissions, admission rules, and independent state readback.
Test AI agent execution controls with a sixteen-scenario offline lab. Separate authorization, target effects, and completion evidence before validating a real platform.
Design an AI agent authorization architecture that separates proposals, policy, execution, and evidence, including indirect paths that can bypass approval.
Define a proposed agent action evidence contract that connects identity, approval, execution, independent observation, and recovery while preserving unresolved outcomes.
Use LLM-as-a-Judge for scoped evaluation without confusing a favorable score with proof or permission. Keep authorization and outcome verification independent.
Explore six dimensions of independent AI assurance, with controls that separate agent judgment, authorization, execution and evidence, plus a proposed benchmark.
Make RAG citations useful evidence rather than decorative references. Validate source identity, claim support, applicability, and disclosure permission while keeping citation destinations under application control.
Carry the effective user’s permissions through the entire RAG workflow. Enforce access before generation, preserve restrictions through caches and history, and test behavior when permissions change.
Treat ingestion, chunking, embedding, and storage as governed data processing. Preserve source restrictions and lineage, control processing routes, and design revocation and recovery before expanding the RAG corpus.
Retire an AI service across its endpoints, identities, data, delegated work, and recovery automation. Verify that it cannot keep acting or silently return through an overlooked deployment path.
Contain compromised AI agents across identity, tools, data, memory, and downstream systems. Reconcile accepted actions, preserve evidence, and use explicit gates for a controlled return to service.
TL;DR An enterprise can build prompt filters, model gateways, audit pipelines, fallback providers, cost controls, and kill switches, yet still be exposed if its AI vendor agreement permits broad data reuse, silent model replacement, opaque subprocessors, weak evidence access, unpredictable pricing, or an unusable exit process. The AI contract is therefore part of the architecture. … Explore: The AI Contract Is Part of the Architecture:…
TL;DR Boards should not approve “AI at scale” as a broad technology initiative. They should approve a bounded portfolio of AI use cases with measurable value, named owners, governed data, production-ready architecture, constrained authority, tested controls, workforce readiness, and a credible exit path. This scorecard gives CEOs and boards 12 questions to ask before enterprise … Explore: The Board-Level AI Readiness Scorecard: 12 Questions CEOs…
TL;DR Shadow AI is not merely unapproved software. An unsanctioned agent, copilot, script, or autonomous workflow can authenticate as a machine identity, call a model provider, read enterprise data, invoke tools, modify systems, and create spend without appearing in a centralized AI inventory. The practical discovery method is evidence correlation. Start with model-provider traffic, provider … Explore: The Shadow AI Beneath the Surface: Finding Unsanctioned…
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