10 AI Governance Software Tools Compared for 2026

Samuel Nagy

VP of Strategic Growth

October 21, 2026

AI governance software falls into three layers: tools that manage the AI inventory and risk process, tools that govern the data and context AI depends on, and tools that check every prompt as it happens. This guide maps ten tools across those layers, what each one covers, and where its scope ends, so you can see which pieces your organization actually needs.

TL;DR

AI governance software works in three layers. Policy and risk platforms run the AI inventory, risk assessments and compliance evidence. Data and context governance makes the data behind AI understood, owned and safe to use. Runtime security checks prompts and responses as they happen. Some tools span two layers: OneTrust and Holistic AI add runtime controls to policy and risk, and Dawiso combines policy and risk with data and context, so each risk assessment rests on real lineage and data classification. The EU AI Act's obligations for stand-alone high-risk AI now start on 2 December 2027, which gives time to build the foundation first.

What Is AI Governance Software?

AI governance software helps an organization answer four questions about its AI: which AI systems and agents it runs, how risky each one is, which rules apply to it, and how to prove those rules are followed. That covers an inventory of models, agents and AI-powered applications, risk assessments, controls mapped to frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, monitoring, and evidence for regulations like the EU AI Act.

The need is real. In IBM's 2025 Cost of a Data Breach research, 63% of breached organizations either had no AI governance policy or were still developing one, and one in five reported a breach caused by shadow AI. The Stanford AI Index 2026 counts 362 AI incidents in 2025, up from 233 a year earlier, and in the IAPP AI Governance Profession Report 2025, 77% of organizations said they are already working on AI governance. For the discipline itself, see AI governance.

The Three Layers of AI Governance Software

"AI governance" covers three different jobs, and they are usually done by different tools. Looking at the market by layer is the quickest way to see what you already have and what is missing.

Layer	Main job	Usually owned by
Policy and risk	AI inventory, risk tiering, assessments, compliance evidence	Risk, compliance, legal, AI office
Data and context	Governed data, lineage, classification and business context for AI	CDO, data governance, data platform teams
Runtime security	Inspect and block prompts, responses and agent actions in real time	CISO, security engineering

There is no single standard way to slice this market. Gartner defines AI governance platforms around an AI inventory, risk management and regulations, automated policy compliance and enforcement at runtime, and interoperability, while governance of the data itself is a separate Gartner market, data and analytics governance platforms. The IAPP AI governance vendor report uses yet another split: policy and compliance, technical assessments and evaluations, assurance and auditing, and consulting. The three layers above follow the job a tool does for you, and several tools span more than one, which each description notes.

The tools below are grouped by their main layer, in no particular order. For each one we describe what it covers and where its scope ends.

Click to enlarge

The table gives a quick overview of which layers each tool covers, based on what each vendor documents.

Tool	Policy and risk	Data and context	Runtime security
IBM watsonx.governance
ServiceNow AI Control Tower
OneTrust AI Governance
Credo AI
Holistic AI
Monitaur
Saidot
Dawiso
Microsoft Purview DSPM for AI
Databricks Unity Catalog
Check Point, Palo Alto Networks

IBM watsonx.governance

Policy and risk. IBM watsonx.governance manages AI across its lifecycle: an inventory of the organization's AI, discovery of unapproved AI tools, policy and obligation mapping, and risk management that acts on risk signals in real time. It maps AI projects to the EU AI Act, NIST AI RMF and ISO/IEC 42001 and connects to other AI platforms such as Azure and AWS.

It is a broad enterprise platform, and like the rest of this layer it governs the AI systems themselves. The definitions, lineage and classification of the data those systems use come from data governance tools, which IBM offers as separate products and other organizations cover with the catalog they already run.

What it covers

AI inventory with discovery of unapproved AI
Policy and obligation mapping with ongoing evidence
Lifecycle risk management

ServiceNow AI Control Tower

Policy and risk. ServiceNow launched AI Control Tower in May 2025 as a central place to govern ServiceNow and third-party AI agents, models and workflows. Governance tasks run as ServiceNow workflows, next to IT and risk processes.

Its value depends on how much of the organization already runs on ServiceNow. Teams outside that ecosystem gain less from the workflow integration.

What it covers

One view of ServiceNow and third-party agents, models and workflows
Governance tasks as ServiceNow workflows
Tracking of AI value alongside risk

OneTrust AI Governance

Policy and risk, with runtime guardrails. OneTrust discovers AI across systems, models, agents, datasets and vendors, assigns owners and lifecycle status, and tiers risk by use case, system or data sensitivity. It includes templates for the EU AI Act, NIST AI RMF and ISO/IEC 42001, and runtime guardrails that filter prompts and outputs and can block, redact or escalate actions, so it reaches into the runtime layer as well.

It comes from the privacy and consent world, so it fits naturally where privacy teams already use OneTrust. Its view of data centers on personal data and sensitivity.

What it covers

AI discovery including third-party AI and vendors
Risk tiering, revalidated when a model or agent changes
Runtime guardrails with personal data redaction

Credo AI

Policy and risk. Credo AI is a dedicated AI governance platform. Its registry catalogs AI systems, agents, vendors and models, and its policy packs turn frameworks such as the EU AI Act and NIST AI RMF into requirements. It treats agents as governed entities with their own risks, such as tool misuse.

Beyond the registry, it runs continuous risk assessment with drift detection and real-time alerts, so risk is tracked after launch as well.

What it covers

Registry of AI systems, agents, vendors and models
Policy packs that translate regulations into controls
Agent risk and control library

Holistic AI

Policy and risk, with testing and runtime controls. Holistic AI discovers models, agents and applications across platforms such as AWS, Azure, GitHub and Databricks, then tests them for security, safety and bias, including red teaming. Controls map to the EU AI Act, NIST AI RMF and ISO/IEC 42001.

Because its guardian agents can block or pause actions in flight, it reaches into the runtime layer as well as the governance process.

What it covers

AI discovery across cloud and code platforms
Testing before launch and in production
Agents that supervise other agents

Monitaur

Policy and risk, industry-focused. Monitaur focuses on regulated industries, with model governance built for insurance and a financial services case study. It covers AI policies and risk assessments, model validation for drift and bias, and internal and third-party AI risk in one system.

The industry focus is the main reason to look at it, and it also narrows the fit for organizations in other sectors.

What it covers

Model governance built for insurance, also used in financial services
Validation for drift, bias and stress
Internal and third-party AI risk

Saidot

Policy and risk, EU-focused. Saidot, based in Helsinki, keeps AI agents, systems, models and datasets in one inventory and supports the EU AI Act, ISO/IEC 42001 and NIST AI RMF with guided workflows. A library of more than 260 risks, 620 controls and 110 policies helps teams assess systems consistently.

Its focus is the regulatory process, especially in Europe. Runtime events from connected platforms can trigger reviews and alerts.

What it covers

One inventory for agents, systems, models and datasets
Risk, control and policy library
Agent registration with risk classification

Dawiso

Policy and risk, plus data and context. Dawiso covers two layers in one platform. Its AI governance solution keeps a centralized list of AI systems and use cases, with each system's purpose, the data it relies on, its validation status and its documentation. A risk assessment evaluates each use case against frameworks such as the EU AI Act, using predefined, expert-designed assessment logic that looks at it from several angles, such as data sensitivity, transparency and human oversight.

What sets it apart is that this inventory sits on the same platform as the data. Every risk assessment asks which data an AI system uses, where it comes from and whether it is personal or confidential. In Dawiso those answers come from data provenance: lineage that tracks the data feeding each model and the sensitivity of that data, with ownership and classification of personal and confidential data. A semantic layer built on the business glossary keeps definitions such as net revenue or churn rate consistent for data and models alike, and the MCP server gives AI agents that governed context, with tools that follow the user's role. Dawiso does not inspect prompts and responses at runtime; that job belongs to the runtime layer below.

What it covers

Centralized list of AI systems and use cases with documentation
Risk assessment against frameworks such as the EU AI Act
Data provenance, sensitivity and lineage for the data feeding each model
Semantic layer and governed context for AI agents over MCP

An AI inventory tells you which models you run. Governed data tells you whether what they read can be trusted. Dawiso keeps both in one place.

Microsoft Purview DSPM for AI

Data and context, security side. Data Security Posture Management for AI in Microsoft Purview shows how Copilots, agents and third-party generative AI apps are used, applies policies that stop sensitive data leaking into prompts, and finds overshared content before Copilot surfaces it. Microsoft has since moved it into a new Data Security Posture Management experience.

Its scope is data security, and its reach is strongest inside Microsoft 365 and Fabric. Cataloging and business glossaries live in other Purview components, so what an organization gets depends on which parts of Purview it runs.

What it covers

Visibility into Copilot, agent and third-party AI activity
Policies that stop sensitive data leaking into prompts
Oversharing assessments for SharePoint and Fabric

Databricks Unity Catalog

Data and context, platform-native. Unity Catalog governs data and AI assets, including tables, functions and models, in one namespace. It tracks lineage from source data to models and dashboards, controls access with privileges and attribute-based policies, and keeps an audit record.

The governance is real, and its scope is the Databricks platform. Data and AI that are not registered in Databricks need a governance layer with wider reach.

What it covers

Models governed alongside data in one namespace
Lineage from source tables to models and dashboards
Attribute-based access policies and audit logs

AI Runtime Security Tools

Runtime controls sit closer to security than to governance. They inspect prompts, responses and agent actions as they happen, and block prompt injection, data leakage or unsafe tool calls. Large security vendors have moved in through acquisitions: Check Point acquired Lakera, and Palo Alto Networks acquired Protect AI and built it into Prisma AIRS. Some AI governance platforms above include runtime guardrails of their own, such as OneTrust and Holistic AI. Dedicated AI security tools go deeper on enforcement for each request, and usually leave the inventory, assessments and evidence regulators ask for to the other layers.

How to Choose AI Governance Software

Start from the gap you need to close first, then test any shortlist against these questions.

Question	Why it matters
Can it find AI you did not register?	Shadow AI and embedded AI features are where unmanaged risk sits.
Does it cover agents as well as models?	Agents act with tools and permissions, which needs its own controls.
Which frameworks are built in?	EU AI Act, NIST AI RMF and ISO/IEC 42001 mappings save months of work.
Does each AI use case link to its data?	Without lineage and classification, a risk assessment rests on assumptions.
Does it fit your platforms?	Platform-native tools govern well inside their platform and stop at its edge.
Who will use it every day?	Risk and legal teams need workflows; data teams need catalog and lineage.
Can it produce audit evidence on demand?	Regulators and auditors ask for records.

If your AI governance program is just starting, buy nothing yet. The AI governance best practices guide covers what to put in place first, and most of it starts with knowing your AI systems and the data behind them.

EU AI Act Deadlines for AI Governance

Many buyer guides still name August 2026 as the high-risk deadline. The Digital Omnibus on AI, Regulation (EU) 2026/1744, published in the Official Journal on 24 July 2026, moved it. The current timeline:

Date	What applies
2 February 2025	Prohibited AI practices and AI literacy
2 August 2025	Obligations for general-purpose AI models
2 August 2026	General application of the AI Act, including the Article 50 transparency obligations
2 December 2026	Marking of AI-generated content for systems placed on the market before 2 August 2026
2 December 2027	Obligations for stand-alone high-risk AI systems
2 August 2028	Obligations for high-risk AI embedded in regulated products

Use the extra time to build the program. An inventory, risk classification and data documentation take longer to put in place than most teams expect. Our EU AI Act article covers the obligations in detail.

Where Dawiso Fits

Dawiso covers two layers of an AI governance stack. For policy and risk, it keeps the centralized list of AI systems and use cases, their documentation and their risk assessments against frameworks such as the EU AI Act. For data and context, it catalogs data across the platforms it connects to, tracks the provenance and sensitivity of the data feeding each model, keeps business definitions consistent in a semantic layer, and serves governed context to AI agents over MCP. Because both live on one metadata platform, each AI use case can be traced to the data and the people behind it.

See it in action

Dawiso AI Governance

Inventory, risk assessment and governed data for every AI use case.

FAQ

What is AI governance software?

AI governance software helps an organization know which AI systems it runs, decide which risks are acceptable, apply policies to them, and prove compliance with regulations such as the EU AI Act. Typical features are an inventory of AI systems and agents, risk assessments, policy and control mapping to frameworks like NIST AI RMF and ISO/IEC 42001, monitoring, and audit evidence.

What are the main types of AI governance software?

There are three layers. Policy and risk platforms (for example IBM watsonx.governance, ServiceNow AI Control Tower, OneTrust, Credo AI, Holistic AI, Monitaur and Saidot) manage the AI inventory, assessments and compliance. Data and context governance tools (for example Microsoft Purview and Databricks Unity Catalog) govern the data AI uses and the context it reads. Dawiso covers both of these layers in one platform. Runtime security tools inspect prompts and responses in real time. Many organizations combine at least two layers.

Do I need AI governance software if I already have data governance?

Data governance covers the data. AI governance adds the AI systems themselves: an inventory of models and agents, their risk level, their intended purpose, human oversight, and evidence for regulators. The two connect, because most AI risk starts in the data, so an AI governance program works best when each AI use case links to the governed data it depends on.

When do EU AI Act high-risk obligations apply?

After the Digital Omnibus on AI (Regulation (EU) 2026/1744), obligations for stand-alone high-risk AI systems apply from 2 December 2027 and for high-risk AI embedded in regulated products from 2 August 2028. Prohibited practices and AI literacy have applied since 2 February 2025, and obligations for general-purpose AI models since 2 August 2025.

How does Dawiso help with AI governance?

Dawiso covers two layers of AI governance in one platform. Its AI governance solution keeps a centralized list of AI systems and use cases with their purpose, data sources, validation status and documentation, and assesses each one against frameworks such as the EU AI Act using predefined, expert-designed assessment logic. On the data side, it tracks the provenance and sensitivity of the data feeding each model, keeps business definitions in a semantic layer, and serves governed context to AI agents through its MCP server.