We implement...
✅ Dataset audits → demographic balance regularly
✅ Bias mitigation → reweighting, augmentation, adversarial debiasing
✅ Evaluation across groups → fairness benchmarks and real-user testing
✅ Transparency artifacts → publish model cards & dataset sheets
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We implement...
✅ Prompt injection defenses → sanitize/validate user input
✅ Model robustness → test against adversarial attacks and data poisoning
✅ Rate limits & monitoring → detect model extraction attempts
✅ Content filtering → red-team outputs for disallowed or harmful content
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We implement...
✅ Framework alignment → map to NIST AI RMF, EU AI Act, ISO/IEC 42001
✅ Continuous monitoring → automate drift detection and error logging
✅ Incident response → close data leaks, bias incidents or unsafe outputs
✅ Regular external audits → do independent red-teaming and compliance checks
✅ User transparency → clear consent forms, data-use disclosures, and opt-outs
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