EU AI Act Compliance: A Practical Guide for Enterprise AI Teams
Everything you need to know about EU AI Act compliance — risk classifications, requirements, timelines, and a practical checklist for enterprise AI teams.
AI Governance & Frameworks
Policies, frameworks, and supervision strategies for governing AI systems at enterprise scale.
27 articles & guides
Latest in AI Governance & Frameworks
AI Governance Maturity Model: Assess and Advance Your Organization's AI Governance
A practical AI governance maturity model with 5 levels to help enterprises assess their current state and build a roadmap to robust AI governance.
Agentic AI Governance: How to Trust and Control Autonomous AI Agents
A comprehensive guide to agentic AI governance — why traditional frameworks fall short and how to build trust, safety, and accountability for autonomous AI agents.
Debugging ML Models with Explainable AI
Machine learning models can have invisible bugs that traditional testing misses. Explainable AI techniques reveal data leakage, data bias, and other problems that undermine model reliability.
Counterfactual vs Attribution Explanations in AI
Two approaches dominate AI explainability: counterfactuals show what would need to change for a different outcome, while attributions quantify feature importance. Understanding both is essential for comprehensive model understanding.
Causality vs Correlation in Model Explanations
Feature importance explanations should surface factors that are causally responsible for predictions. Confusing correlation with causation leads to misleading explanations and poor decisions.
Building Trust with AI in Financial Services
Financial institutions face four major challenges operationalizing AI: lack of transparency, production monitoring, potential bias, and compliance barriers. Addressing all four is essential for trustworthy deployment.
Best Practices for Responsible AI Deployment
Responsible AI is not a one-time audit. It requires ongoing accountability, human oversight, and systematic practices embedded into how organizations develop and deploy AI systems.
Guides & Definitions
What is AI Ethics?
AI ethics is the practice of maximizing the benefits of AI while reducing harm. It's about respecting people's rights, ensuring fairness and transparency, protecting privacy, and making sure accountability doesn't get lost in the model maze.
What is AI Governance?
AI governance is the set of policies, processes, standards, and tools that coordinate stakeholders to ensure AI is built, deployed, and managed responsibly.
What is AI Risk Management?
AI risk management is the systematic process of identifying, assessing, and mitigating risks across the AI lifecycle to protect your organization from technical, operational, and regulatory failures.
What is AI Trust Validation?
AI trust validation is the end-to-end process of establishing evidence that an AI system is worthy of calibrated trust for specific users, tasks, risks, and contexts.
What is Responsible AI?
Responsible AI is the practice of developing and operating AI systems that are safe, fair, transparent, and accountable—not just in principle, but in measurable, operational terms.
See how Swept AI enables governance through supervision
Continuous supervision and audit-ready proof that your AI meets governance and compliance requirements.
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Techniques for building safe AI systems with runtime supervision, guardrails, bias protection, and risk mitigation.
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Supervise AI performance in production with observability, drift detection, and operational monitoring.
7 articlesAI Evaluation & Testing
Methods and tools for rigorously evaluating AI models before deployment and supervising them after.
24 articlesAI Compliance & Regulation
Navigate AI regulations, compliance requirements, and audit readiness with continuous supervision.
8 articlesAgentic AI Trust & Governance
Supervising autonomous AI agents with trust frameworks, safety boundaries, and multi-agent oversight.