Directory AI & ML PlatformsAI Governance & SafetyFiddler AI

Fiddler AI

About Fiddler AI

Fiddler AI is a control plane for enterprise AI agents. It covers evaluation, monitoring, policy enforcement, and governance. It works with Azure OpenAI, Amazon Bedrock, LangGraph, and more than 100 other providers.

Products

Fiddler Centor Models: Run guardrails and evaluations in the customer's environment, with no external LLM API calls.
Fiddler Guardrails: Enforces policy inline on agent requests and responses.
Fiddler AI Observability: Observability that can run inside Amazon SageMaker Studio.

Used for

Tracking agent actions, tokens, and costs by user and team.
Redacting PII, PHI, and secrets from prompts and responses.
Recording enforcement decisions as audit evidence.

Who it is for

Organizations deploying AI agents, including in financial services, healthcare, insurance, and government.
Software and AI engineers.
Data scientists.
Platform, DevOps, security, and compliance teams.

From Fiddler AI's own website, read 2026-10-23. How we build listings.

How to evaluate AI Governance & Safety

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Fiddler AI. It comes from our AI Governance & Responsible AI buyer guide.

25%

AI Inventory & Lifecycle Governance

Automated discovery and a registry of every model, LLM/prompt, agent, and embedded-AI feature (including shadow and third-party AI); risk tiering by use case; intake-to-retirement workflow with approvals, deployment gates, and named owners; versioning and change control

25%

Regulatory Mapping & Compliance Evidence

Out-of-the-box, maintained policy packs for the EU AI Act, NIST AI RMF, ISO 42001, SR 11-7, and sector rules (e.g. NYC LL144); control mapping and gap analysis; one underlying assessment that satisfies many frameworks; audit-ready, exportable evidence and model cards

20%

Model Monitoring & Explainability

Production monitoring for drift, performance decay, and data quality; bias and fairness testing across protected groups; explainability (e.g. SHAP / feature attribution) for tabular and NLP models; the depth of this domain is what separates observability tools from pure GRC

15%

GenAI & Agentic Oversight

LLM evaluation and guardrails (hallucination, toxicity, PII leakage, prompt-injection and jailbreak defense); agent discovery, runtime policy enforcement, and a logged trail of agent actions and the authority behind them; red-teaming and continuous evaluation of agent traces

10%

Integration & ML-Stack Fit

Connectors to your model platforms (SageMaker, Vertex AI, Databricks, Dataiku, MLflow, Bedrock), CI/CD and registries; API and policy-as-code coverage; identity (SSO/RBAC); fit with existing GRC and data-security tooling rather than yet another silo

Human-in-the-Loop & Accountability

Cross-functional workflows that reach risk, legal, and business owners (not just data scientists); reviewer sign-off and attestations; issue and exception tracking; reporting that a board or regulator can read, with a defensible audit trail

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