Fiddler for AI Governance, Risk Management, and Compliance (GRC)

Strengthen oversight and compliance across generative and predictive applications
Industry Leaders’ Choice for AI Observability

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.

One of the things that was appealing to IAS about Fiddler was its ability to customize the monitoring to specific model type, data volume and desired insights. Additionally, the dashboard views, automated alerting and ability to generate audit evidence also factored into the decision to work with Fiddler.
Kevin Alvero
Chief Compliance Officer, IAS

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.

Bar chart showing average racism and sexism scores for prompt and response safety evaluations over several days, highlighting fluctuations and trends in AI model safety metrics.

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

Fairness dashboard showing demographic parity segmented by race, disparate impact compared against Caucasian applicants, group benefit by gender, and group benefit by race, providing visual insights into model fairness and potential biases across various subgroups.

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

Dashboard showcasing LLM Response Faithfulness Tracker, Data Drift metrics, Chat Embedding Drift, and PII Leakage incidents, providing detailed visualizations to monitor and audit AI chatbot behavior and compliance with data integrity and privacy standards.

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

Line chart displaying intersectional fairness metrics over time, tracking data drift using Jensen-Shannon Distance (JSD) for various protected attributes, such as ownership, income type, and education type, within a credit approval model.