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• Shadow AI in the Enterprise: Detecting, Governing, and Securing Unauthorized AI Tool Use

  How to detect, govern, and secure unauthorized AI tool use in the enterprise — before sensitive corporate data leaves through AI prompts.

  Jul 11, 2026 compliance security-architecture threat-modeling incident-response

• How AI Safety Evaluations Are Gamed: Sandbagging, Context Drift, and Eval Design Gaps

  How AI safety evaluations are gamed via sandbagging, context drift, and benchmark saturation—and what better evaluation design looks like for practitioners.

  Jun 30, 2026 ai-safety benchmarks red-teaming compliance threat-modeling

• Benchmark Contamination and the False Assurance Problem in AI Safety Evaluations

  AI safety benchmarks like HarmBench, ToxiGen, and AdvGLUE are increasingly used to assert that a model is safe. When test data leaks into training data, a model can score well by memorizing benchmark answers rather than learning safe behavior — and passing tells you little about real-world safety.

  Jun 29, 2026 ai-security benchmarks contamination evaluation compliance red-teaming