14 Best AI Governance Platforms and Tools in 2025

Fast Facts on AI Governance Platforms

• AI governance platforms help organizations manage AI risks by defining, monitoring, and enforcing policies for transparency, compliance, and safety across the AI lifecycle.

• The market is projected to grow from $227 million in 2024 to $4.83 billion by 2034, driven by generative AI adoption, evolving regulations like the EU AI Act, and high-profile AI misuse incidents.

• The strongest platforms cover policy definition, lifecycle oversight, enterprise system integration, real-time observability, automated policy enforcement, and AI-specific risk management.

• The top 14 tools vary in focus, from Knostic’s identity-centric LLM controls to Monitaur’s lifecycle compliance and Prompt Security’s LLM red-teaming, allowing organizations to match tools to their specific needs.

• Selecting the right platform requires aligning governance goals with business priorities, confirming core capabilities, running production-like pilots, and validating security and compliance fit before full deployment.

How We Picked These Best AI Governance Platforms

Although more and more enterprises are deploying AI, the development of AI governance platforms has not kept pace. The global AI governance market, valued at just $227 million in 2024, is projected to reach $4.83 billion by 2034, at a staggering CAGR of 35.7%, reflecting the urgent demand for tools that ensure transparency, compliance, and risk control. Multiple forces drive this surge: the accelerating adoption of generative AI across industries, the complexity of managing AI in compliance with new frameworks like the EU AI Act and NIST AI RMF, and high-profile incidents where AI systems have exposed sensitive data or produced harmful outputs. These factors reveal why many enterprises struggle to keep governance efforts aligned with the pace of AI rollout, often relying on fragmented or outdated tools.

Addressing these gaps is essential to avoiding regulatory penalties, reputational damage, and uncontrolled AI-related risks. For this analysis, we applied clear, technical criteria. We prioritized platforms that:

• Span the full AI lifecycle - Platforms must support policy definition, monitoring, enforcement, auditing, and drift detection. This lifecycle oversight is essential. ModelOps emphasizes the need for real time governance across production, evaluation, and model testing . While MLOps focuses on the operationalization of machine learning models, ModelOps extends beyond ML to govern all AI models, including rules-based systems and generative AI, throughout their lifecycles as outlined in Forrester’s 2024 ModelOps framework.
• Enable enterprise-level integration - Tools must integrate with existing AI, data, security, and identity systems. Strong governance depends on seamless connectivity across systems and teams, not isolated tools.
• Provide observability and anomaly detection - Real-time monitoring, bias detection, drift alerts, and log trail capabilities are essential. Observability frameworks help maintain compliance, assist in audits, and align with regulations like the EU AI Act.
• Automate risk-aware policy enforcement - Governance must adapt dynamically. The Unified Control Framework, for example, recommends a concise set of controls (42 in total) that address multiple risks and automate compliance needs, in order to simplify enforcement at scale.
• Address AI-specific risks - Governance must explicitly account for biases, lack of transparency, and generative AI behaviors such as hallucinations. Yet, reports show just 28% of organizations test for bias and only 22% test interpretability, emphasizing the importance of platforms that do.

Top 5 Enterprise AI Governance Platforms Snapshot

Ratings in this table (“Strong,” “Moderate,” “Basic”) are based on a review of publicly available vendor documentation, product demos, verified case studies, and alignment with established governance frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001. Where vendor claims were not independently verifiable, we relied on feature parity with comparable solutions in the same category.

14 Best AI Governance Platforms: Detailed Analysis

Knostic

Overview 

Knostic enforces real-time need-to-know access policies across AI systems like Copilot, Glean, and Gemini. It simulates prompts from real users to detect and prevent oversharing of sensitive information from sources such as SharePoint, OneDrive, and Teams.

Strengths

• Automated detection of oversharing: Knostic flags sensitive content and enforces access policies to prevent it from reaching unauthorized users.
• Simulation-driven risk exposure: It runs prompt campaigns using real user scenarios to uncover hidden data inference paths that static access controls may miss.
• Rapid, non-disruptive deployment: Integrates with Microsoft 365, Glean, and Copilot within hours or days, not months, offering fast time to oversight.
• Compliance alignment: Supports enforcement for HIPAA and GDPR through inferred behavior tracking and audit trail generation.
• Funding validates problem focus: Backed by leading security investors, including Silicon Valley CISO Investments, to tackle AI-related data leakage and oversharing, signaling strong market validation.

Weaknesses

• Specialized use case: Its features are customized towards enterprise AI governance and may be overkill for simpler or smaller-scale AI deployments.

Prompt Security

Overview

Prompt Security focuses on securing AI applications from prompt injection, data leakage, and unauthorized access. It combines LLM red-teaming with real-time authorization controls to detect and block malicious prompt activity. The platform integrates into CI/CD pipelines and production environments to continuously test and support AI security policies.

Strengths

• Specialized in AI prompt security: Actively tests against prompt injection, prompt leaking, and malicious prompt chaining.
• Red-team as a service: Provides automated adversarial testing for LLM applications in development and production.
• Real-time policy enforcement: Supports dynamic access control and prevention of sensitive data exposure.
• Integrates with developer workflows: Works with existing CI/CD and DevSecOps pipelines to catch risks early.

Weaknesses

• Narrow focus: Primarily addresses LLM security, not the full AI governance lifecycle (policy management, compliance reporting, etc.).
• Limited public benchmarks: Few independent, third-party performance evaluations are published.

Lasso Security

Overview

Lasso Security provides a low-code AI governance and security platform designed for both compliance enforcement and risk mitigation. It provides data access controls, policy orchestration, and AI application security. The platform is aimed at enterprises that need to meet the EU AI Act and U.S. AI governance frameworks.

Strengths

• Compliance-driven policy engine: Built to help organizations align with GDPR, CCPA, and the EU AI Act requirements.
• Low-code customization: Enables security and compliance teams to deploy governance workflows without heavy coding.
• Integrated AI risk scanning: Assesses AI models and applications for vulnerabilities, bias risks, and compliance gaps.
• Multi-jurisdiction coverage: Supports governance policies across the U.S., EU, and other regulatory environments.

Weaknesses

• The governance scope is limited to supported regulations. May require custom work for niche industry rules not directly supported.
• There is less emphasis on AI model lifecycle management compared to some specialized governance platforms.

HiddenLayer

Overview

HiddenLayer delivers a model-centric AI security platform that protects machine learning models from adversarial attacks, data poisoning, and theft. It applies threat intelligence, runtime detection, and posture management to secure AI assets in enterprise environments.

Strengths

• Model-specific security: Protects against adversarial ML attacks, including evasion and extraction.
• AI threat intelligence: Maintains an active database of AI/ML security threats for proactive defense.
• Runtime protection: Monitors deployed models for suspicious activity and data manipulation attempts.
• Industry partnerships: Works with major cybersecurity vendors to integrate AI-specific threat defense.

Weaknesses

• Focus on model security: Less emphasis on governance features such as compliance dashboards or policy management.
• Technical deployment: May require in-house ML engineering expertise to utilize capabilities fully.

AIM Security

Overview

AIM Security focuses on AI security posture management, mapping risks from development to production. It provides model scanning, security benchmarking, and risk tracking to help enterprises identify and mitigate vulnerabilities in AI pipelines.

Strengths

• End-to-end AI risk visibility: Monitors risks from data preparation through model deployment.
• Policy and compliance alignment: Designed to integrate governance controls that align with emerging AI regulations.
• Model vulnerability detection: Scans for adversarial weaknesses, prompt injection, and unsafe outputs.
• Developer-centric integration: Works in CI/CD pipelines for continuous AI security validation.

Weaknesses

• Focus on security posture: Less emphasis on advanced policy orchestration or role-based access controls.
• Limited public benchmarks: Few independent performance metrics are published for comparative analysis.

Opsin Security

Overview

Opsin focuses on preventing AI data oversharing and securing rollouts of tools like Microsoft 365 Copilot and Glean. It provides proactive risk discovery, real-time usage monitoring, and policy-based remediation. 

Strengths

• Copilot guardrails and monitoring: Connects to Microsoft 365 Copilot in minutes to surface risky interactions and guide remediation before broad rollout.
• Glean hardening: Adds enforcement and remediation where Glean’s built-in permissions may allow oversharing; helps align access to “right user, right data.”
• AI readiness assessments: Rapid assessments to test tools (Copilot, Gemini, Glean) and return actionable risk insights.
• Industry solutions: Targeted playbooks for regulated sectors (e.g., finance, healthcare) to fix oversharing at the source and reduce burden on IT/security.
• Customer proof point: Case study (Culligan) showing mapping of sensitive exposures and policies put in place before Copilot rollout.

Weaknesses

• Scope: Public materials emphasize M365/Copilot and enterprise search scenarios; fewer details on full model-lifecycle governance (e.g., bias/drift testing).
• Independent benchmarks: Limited third-party performance/efficacy benchmarks available publicly.

Redactive AI

Overview

Redactive is an enterprise AI security platform that gives contextual, permissions-aware control over what employees, agents, and AI apps can access. It manages shadow-AI usage, prevents sensitive data from being shared in prompts, and is positioned for Copilot enablement and regulated environments.

Strengths

• Permissions-aware controls for AI assistants and agents; focuses on document- and chunk-level assurance of access. (Demo explainer: “Permissions Assurance.”)
• Shadow-AI visibility and prevention to stop sensitive data from entering unapproved tools. 
• Already in use by two large Australian financial services institutions, scaling generative AI safely to 500+ users each.
• External validation: Reported funding and enterprise focus.

Weaknesses

• Public technical docs are light; granular deployment details live in demos/briefings. (based on publicly accessible materials)
• Young vendor; limited third-party benchmarks in the public domain.

Pangea

Overview

Pangea offers security guardrails via APIs and gateways for AI apps. Core services include Secure Audit Log (tamper-evident logging), AI Guard (PII redaction, prompt-injection detection, toxicity filters), and Prompt Guard (jailbreak/policy violation blocking).

Strengths

• Tamper-evident audit logging with SDKs, examples, and Postman collections; used for compliance-grade evidence. 
• AI Guard / Prompt Guard provides prompt-injection detection, malicious entity filtering, PII/sensitive-data redaction, and jailbreak blocking.
• Developer-first approach (SDK/API) to add guardrails with “a few lines of code” or via gateway deployment.

Weaknesses

• Requires engineering ownership due to API-centric approach; fewer native “GRC dashboard” features than full-suite governance platforms.
• Unsubstantiated efficacy claims (e.g., detection accuracy) should be validated in your environment; rely on POC metrics.

Harmonic

Overview

Harmonic provides AI data security and governance tools aimed at discovering, classifying, and protecting sensitive data used by GenAI systems. It focuses on Shadow AI detection, real-time data masking, and zero-touch policy enforcement across enterprise SaaS and AI integrations.

Strengths

• Shadow AI discovery: Detects unauthorized AI tool usage and maps sensitive data flows.
• Data masking and filtering: Automatically removes or masks sensitive data before it reaches LLMs.
• Raised $17.5M in Series A funding led by Next47, bringing total funding to $26M, with the commercial offering adopted by several enterprise customers.

Weaknesses

• Limited public deployment data: Few case studies or third-party benchmarks available.
• Younger vendor with less maturity compared to legacy governance providers.

Oleria

Overview

Oleria focuses on continuous identity governance and least-privilege enforcement for both human and AI identities. It applies IGA principles to manage access in dynamic environments, including AI assistants and services.

Strengths

• Continuous access reviews: Automatically adjusts privileges as roles, contexts, or AI system permissions change.
• AI identity integration: Extends governance beyond human users to include AI service accounts and agents.
• Cloud-native design: Works with modern SaaS and cloud-first identity infrastructures.

Weaknesses

• Specialized scope: Strong in identity governance, but lighter on model monitoring, bias detection, or AI-specific risk analysis.
• New player: Limited long-term operational track record.
  

Monitaur

Overview

Monitaur offers model lifecycle governance for AI and ML systems, focusing on documentation, auditability, and compliance tracking. It is used in regulated industries to ensure ongoing compliance with standards like the EU AI Act, FTC AI guidelines, and sector-specific rules.

Strengths

• Full lifecycle coverage: Supports documentation, monitoring, and audit of models from development to retirement.
• Regulatory alignment: Built to help comply with the EU AI Act, OCC guidance, and other AI governance standards.
• Explainability tools: Support transparency requirements with model decision documentation.

Weaknesses

• Primarily compliance-focused: Less emphasis on real-time threat detection or automated access control.
• Integrations: Publicly listed integrations are fewer compared to some broader AI GRC platforms.

Truyo

Overview

Truyo specializes in privacy-first AI governance with tools for AI risk assessment, bias detection, and compliance validation. Initially known for privacy automation under CCPA and GDPR, it has extended its platform to address AI-specific risks.

Strengths

• Privacy by design: Built on privacy compliance frameworks (GDPR, CCPA, CPRA).
• AI bias detection: Identifies and flags bias in AI model outputs.
• Risk and compliance dashboards: Consolidate audit readiness in regulated sectors.

Weaknesses

• Narrower AI focus: Strong focus on privacy and bias, but fewer features  for security posture or threat detection.
• Integration detail: Fewer public examples of integration with MLOps pipelines.

Oso

Overview

Oso is an open-source policy-as-code framework that enables developers to implement granular authorization in AI and non-AI applications. It provides a policy language (Polar) and supports RBAC, ReBAC, and custom models.

Strengths

• Open-source flexibility: Free core framework with an active developer community.
• Policy-as-code: Enables codified, version-controlled authorization rules.
• Multiple access models: Supports role-based, relationship-based, and custom models in the same application.

Weaknesses

• Developer-heavy adoption: Requires coding expertise; not a no-code platform.
• Limited built-in AI governance tools: Primarily an authorization layer; developers must implement AI-specific controls.

Permit.io

Overview

Permit.io delivers full-stack authorization with UI-based policy management. It supports RBAC, ABAC, and ReBAC, and provides AI-specific policy templates to secure GenAI apps.

Strengths

• Low-code policy editor: Lets teams configure and deploy access rules without deep coding.
• Multi-model access control: Combines RBAC, ABAC, and ReBAC for flexible governance.
• AI-ready templates: Prebuilt policy blueprints for AI application scenarios.

Weaknesses

• General-purpose focus: Strong for access control but lighter on lifecycle AI governance functions like model drift monitoring.
• Integration breadth: Smaller ecosystem compared to long-standing IAM providers.

How to Pick Your Ideal AI Governance Platform

Choosing the right AI governance platform starts with understanding where traditional security controls fall short in the age of LLM-powered search. The ideal solution should not only align with your compliance needs and access control models but also address inference risks.

Identify Your Business and Governance Goals

Start by mapping your organization’s AI use cases to its strategic and compliance priorities. The NIST AI Risk Management Framework recommends aligning governance goals with the intended purpose and context of AI systems. If you operate in regulated sectors, define how the platform will help you meet industry-specific obligations such as HIPAA for healthcare, PCI DSS for payments, or GDPR for personal data. The OECD AI Principles stress the need to set measurable objectives for transparency, accountability, and fairness. Having this clarity ensures the platform’s governance features are matched to real operational needs.

Identify Non-Negotiable Capabilities

List the capabilities that the platform must have in order to be viable. The EU AI Act highlights core requirements like risk classification, continuous monitoring, record-keeping, and human oversight. For LLMs, non-negotiable functions may include access control, data masking, and prompt logging. NIST’s guidance emphasizes secure data handling, audit trails, and the ability to detect bias or drift in models. Without these capabilities, the platform will not satisfy minimum governance or security standards.

Weight & Score the Long List

Once you have a long list of possible platforms, apply a scoring system. Gartner’s 2024 AI governance tooling guidance suggests weighting categories like integration, scalability, compliance readiness, and total cost of ownership. Assign higher weights to the features most critical to your sector and risk profile. Keep the scoring process consistent and evidence-based to avoid bias in the selection.

Run a Proof-of-Concept in Production-Like Conditions

Before committing, run a pilot or proof-of-concept in an environment that mirrors your production setup. According to Forrester’s Q2 2024 AI Risk Report, many governance gaps only emerge under realistic workloads and data flows. Simulate actual user interactions, monitor how the platform enforces policies, and observe its performance under load. This step validates both functionality and operational resilience.

Validate Security & Compliance Fit

Finally, verify that the platform meets your security and compliance needs through testing and documentation review. The Cloud Security Alliance recommends reviewing vendor SOC 2 reports, penetration test results, and evidence of regulatory audits. For compliance-heavy sectors, confirm that the platform can produce the documentation required for audits under frameworks like ISO/IEC 42001 (AI Management System) or sector-specific laws. Involve internal audit or compliance teams to review governance and reporting outputs before final selection.

Common Pitfalls to Avoid

Many organizations underestimate risks when procuring AI governance software. The NIST AI RMF warns that poor due diligence can result in security gaps or governance breakdown.

Some platforms make it costly or technically difficult to switch providers later. Review API availability and data export formats to confirm portability. The OECD AI Principles emphasize interoperability to reduce dependency risk.

A platform that works for one business unit may fail under enterprise-wide AI adoption. Assess vendor performance benchmarks and architecture for high-volume, multi-model environments. The EU AI Act requires governance to remain effective across the entire AI lifecycle, regardless of scale. Vendors may advertise adherence to frameworks like ISO/IEC 42001 or SOC 2 without undergoing formal certification. Verify proof of accreditation and check against accredited registries. The Cloud Security Alliance advises requesting SOC 2 reports, penetration test summaries, and regulatory audit evidence.

The Knostic Advantage

Knostic addresses a security gap that traditional tools cannot fully solve: governing the “knowledge layer” between shifting enterprise data and AI-generated insights. While Data Loss Prevention tools protect files, and policy systems like Microsoft Purview monitor direct data access, they struggle to detect when an AI system infers restricted answers from multiple sources.

Knostic enforces real-time, context-aware access policies to prevent oversharing by LLMs such as Microsoft Copilot and Glean. Its continuous, automated audits run across supported enterprise platforms to detect when AI tools can expose sensitive information. The platform maps actual knowledge access patterns, builds need-to-know policies that adapt dynamically to user roles and business context, and generates a detailed audit trail, even for answers synthesized from multiple restricted datasets.

Integration with Microsoft 365 and other supported enterprise AI tools does not require infrastructure redesign, so organizations can adopt AI safely and quickly. These capabilities help reduce compliance risk under regulations such as GDPR, HIPAA, and FINRA.

What’s Next

Security and compliance teams can schedule a demo to explore how Knostic’s knowledge oversharing detection and real-time controls work in their environment. The sooner these protections are in place, the sooner enterprises can unlock productivity from AI without risking sensitive data exposure.

FAQ

• What are AI governance platforms?

AI governance platforms are tools that help organizations define, enforce, and monitor policies for the safe and compliant use of artificial intelligence. They often include features for access control, bias detection, audit logging, and regulatory compliance tracking.

• What are the most critical capabilities of AI governance tools?

The main capabilities include policy management across the AI lifecycle, integration with existing identity and security systems, real-time monitoring for risks like bias or oversharing, and automated compliance reporting to meet standards like ISO/IEC 42001 or NIST AI RMF.

• What are the best AI governance tools 2025?

Several strong AI governance platforms exist in 2025, each with different strengths depending on enterprise needs. For example, for enterprises using Microsoft Copilot or similar LLM tools, Knostic offers a verified, need-to-know-based governance model that focuses on preventing AI oversharing. Other platforms, such as Monitaur or Prompt Security, specialize in areas like model documentation or LLM red-teaming, respectively.