Fiddler for AI Governance, Risk Management, and Compliance (GRC)
Achieve Quality and Transparency Standards for LLM and ML Deployments
As AI advances, policies such as the EU AI Act, the AI Bill of Rights, and the CA AI Bills will continue to be introduced to enforce governance, risk, and compliance regulations (GRC). These regulations aim to increase trust and transparency in AI systems and protect consumers from harmful or biased outcomes.
By implementing model monitoring, explainability, and governance, enterprises can innovate in a responsible manner, safeguard data integrity and ensure that LLM and ML deployments comply with evolving AI regulations.
Companies Trust Fiddler for GRC
Fiddler’s AI Observability platform helps enterprises generate the necessary evidence to comply with stringent AI regulations and GRC, build trust in LLM and ML applications, and establish responsible AI practices.
AI Governance and Compliance
Establish AI governance and compliance practices to align LLM and ML deployments with legal, ethical, and operational standards at every stage of the responsible AI maturity journey.
- Quickly respond to new regulations and GRC guidelines with LLM and ML monitoring evidence and insights
- Stay compliant by tracking critical metrics — such as hallucination, safety, privacy, and bias in LLMs, along with performance, accuracy, drift, and bias in ML models
- Detect and analyze data drift across structured and unstructured data (e.g., NLP, computer vision) to understand impacts on model behavior.
Learn how Fiddler helps enterprises maintain compliance with emerging AI regulations
AI Risk Identification and Mitigation
Proactively assess and mitigate model risks to prevent potential negative impacts on end-users and the enterprise.
- Build a robust model risk management (MRM) framework with greater model transparency and explainable AI to meet periodic reviews, including those by the Federal Reserve and OCC’s SR 11-7 guidelines
- Assess risks, resolve, and mitigate model issues such as model drift, bias, privacy breaches, and unfair outcomes
- Avoid financial risks, fines, or breaches by gaining granular insights into model changes, and receive alerts on issues as soon as they are identified
Learn how to create custom reports for MRM and compliance reviews in Fiddler
AI Transparency, Documentation, and Auditability
Facilitate audits and compliance with comprehensive model monitoring evidence and documentation.
- Generate evidence for audit trails from historical monitoring data to build trust with Risk and Compliance, Trust and Safety teams, and third-party stakeholders
- Support compliance across the LLMOps and MLOps lifecycle with insights on LLM outputs, and ML predictions through global- and local-level explanations
- Maintain detailed documentation to enhance accountability and transparency across AI deployments
Learn how Integral Ad Science scales transparent and compliant AI products using Fiddler
Ethical and Responsible AI Practices
Integrate ethical AI practices to deliver transparent, trustworthy, and equitable outcomes for all users and organizations.
- Minimize compliance challenges, legal risks, and reputation damage by preventing model bias towards specific entities or user groups
- Enable teams to identify and address fairness issues, such as tracking intersectional fairness, across the model lifecycle
- Analyze outcomes across intersections of various protected attributes to ensure fairness
Explore how Fiddler tracks fairness and bias in predictive and generative AI
