Explore concise research briefs that translate technical AI risks into practical, enforceable policy. We focus on capability evaluations, deployment context, transparency, and human oversight so that rules protect people while keeping room for innovation. Each article includes actionable checklists you can adapt to your organization.
General-purpose models can be repurposed for sensitive tasks. We propose thresholds for reasoning, generation, and autonomy that trigger additional safeguards. This keeps low-risk uses flexible while ensuring higher-stakes deployments meet rigorous testing and oversight.
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Model impact depends on where and how it is deployed. We outline a simple matrix that combines capability with domain sensitivity to assign risk levels. The matrix maps to clear controls, documentation, and human review where appropriate.
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Clear artifacts connect data lineage, evaluation results, and intended use. We share a model card outline, a data inventory checklist, and tips to capture safety tests with minimal friction so that teams can document once and reuse across audits.
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A lightweight incident workflow helps teams learn quickly and protect users. We cover severity levels, notification windows, and transparency practices that close the loop between developers, deployers, and regulators.
Read overviewClear user notices and AI interaction labels build trust. We present examples of plain-language disclosures, opt-out options, and accessible FAQs that meet legal needs without overwhelming people.
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Small teams need clarity without heavy process. We outline a minimal control set for low and medium risk systems, plus references to shared tools and test suites that reduce cost while meeting expectations.
Read overviewWe run focused sessions for executive, product, and compliance teams. Leave with a draft risk matrix, a disclosure plan, and a roadmap to pilot guardrails within weeks.