What Are AI Governance Tools?

AI governance tools are platforms that help organizations to centrally enact, manage, and enforce AI usage policies across data, models, agents, and applications. With growing AI usage – including autonomous agents, GenAI, and AI capabilities embedded in SaaS tools –  organizations face expanding security risks that must be managed via governance policies and security controls.

The role of AI governance tools isn’t to replace policies but to implement and enforce them via automation, monitoring, and technical controls. These solutions act as a bridge between an organization’s high-level AI governance and security goals and how they’re implemented and enforced in day-to-day operations.

How Do AI Governance Tools Fit Into Enterprise AI Strategy?

As organizations move from interacting with GenAI chatbots to embedding autonomous AI in critical workflows, AI governance becomes more critical than ever. Implementing responsible AI principles requires a deep understanding of an organization’s AI usage and how to implement guardrails and security controls to put these principles into place on a day-to-day basis.

AI governance tools help organizations to move from high-level policies and goals to the technical controls that enforce them. These solutions should integrate with the enterprise’s existing risk, compliance, and security processes, not act as another standalone, siloed point solution.

AI Governance Tools in the Context of AI Governance Programs

AI governance tools don’t take away the responsibility for defining governance programs. Instead, they provide the capabilities and tools needed to support and enforce them, including:

• Discovery
• Policy enforcement
• Monitoring
• Reporting

These capabilities are essential for board-level and executive-level AI oversight, bridging the gap between high-level policy documents and measurable controls and metrics.

What Problems Do AI Governance Tools Help Solve?

AI governance tools help organizations to implement comprehensive policies that address all of their AI usage. Key challenges that they help to address include:

• Visibility Gaps: The diversity of AI solutions (GenAI, agents, AI-powered SaaS tools, etc.) makes AI monitoring and management complex.
• Shadow AI: Unauthorized use of AI tools can create risks around data security and stability, and security of critical workflows.
• Data Leakage: GenAI and SaaS tools may reveal sensitive data to unauthorized third parties.
• Non-Compliant AI Use: Non-compliance with corporate AI and security policies could place the organization out of compliance with GDPR, CCPA, and the EU AI Act.

Managing Shadow AI and Unapproved AI Tools

Shadow AI refers to the unauthorized use of AI-powered tools. This includes GenAI chatbots, AI coding assistants, SaaS solutions with AI capabilities, autonomous agents, and other solutions.

Use of these tools without approval creates significant data security and regulatory compliance risks for the business. AI governance tools can help to automatically discover AI tools in use within the business, profile their usage, and provide data to help inform decisions about managing them, such as blocking, restricting, or onboarding them under policy.

Reducing Data Leakage and Privacy Risk

Ungoverned AI use can expose sensitive corporate data in various ways. Users may include corporate or customer data in prompts or training data, and AI systems could generate content or take actions that expose sensitive information to unauthorized users.

AI governance tools can help to manage this risk by monitoring data flows into and out of AI systems. This enables the organization to filter out sensitive data and align with data protection requirements such as GDPR, CCPA, and the EU AI Act.

Supporting Regulatory, Ethical, and Internal Policy Compliance

As AI usage grows, regulators are implementing requirements for auditable controls to manage the potential threats associated with AI. This includes the potential for data leaks, biased outputs, and downtime of business-critical workflows.

Compliance with the EU AI Act and the NIST AI RMF requires deep insight into and control over the actions of AI systems. AI governance tools help to centralize evidence, logs, and policy mappings that legal, risk, and audit teams can use during assessments.

What Core Capabilities Define Modern AI Governance Tools?

AI governance tools are designed to help companies overcome the challenge of translating high-level policy goals into technical controls. Approaches to this can vary, but there are a few core capabilities that show up across integrated AI governance solutions, including discovery, classification, policy definition, enforcement, monitoring, incident response, and reporting.

AI and Data Asset Discovery

AI and data asset discovery is essential to addressing an organization’s Shadow AI and visibility challenges. Without a clear understanding of the organization’s AI usage, it’s impossible to secure and align it with compliance requirements.

AI discovery solutions will automatically work to identify AI models, inference endpoints, AI SaaS apps, AI agents, and data sources feeding them across the enterprise, providing a comprehensive inventory for tools to manage and policies to govern. However, as an organization’s AI usage evolves, these inventories rapidly become outdated, making ongoing discovery vital to maintaining up-to-date visibility.

Policy Definition, Orchestration, and Enforcement

AI governance tools centralize the management of an organization’s AI policies, reducing the risk of policy and security gaps due to siloed, standalone security tools. These AI governance platforms maintain central policy catalogs that define who can use which AI systems, with what data, and under which constraints.

In addition to defining policy, AI governance platforms also need the ability to enforce it. For this reason, platforms require deep integration with the organization’s existing security stack. For example, AI governance deployed as part of a SASE platform can natively use ZTNA, CASB, DWG, DLP, and other integrated capabilities to enforce corporate AI policies across the entire corporate WAN.

Monitoring, Alerts, and Reporting for AI Risk

In addition to enforcing corporate AI policies, AI governance tools should also provide the organization with insight into its AI risk exposure. This includes surfacing suspicious AI usage, policy violations, and drift against defined guardrails.

For example, a platform may generate alerts regarding excessively sensitive data in prompts, anomalous access to AI agents, or unsafe model responses flagged for review. By doing so, it enables the business to take preventative action upstream, such as performing additional training or limiting access for risky users.

How Does AI Policy as Code Work in Practice?

AI policy as code implements the principles of infrastructure as code (IaaC) for AI policies and guardrails. Under this model, corporate AI policies are defined in a machine-readable format, enabling them to be tested and deployed directly. By doing so, DevSecOps teams can version control AI policies, include them in CI/CD pipelines, and validate them before deployment.

Encoding Guardrails as Machine-Readable Rules

Implementing AI policy as code involves writing AI guardrails as machine-readable rules. Key elements include:

• Allowed AI services
• Data classification rules
• Model access policies
• Prompt restrictions
• Regional compliance constraints

Implementing policies as code helps to prevent configuration drift as policies and their implementations diverge. Additionally, AI governance policies become more repeatable since code samples can be reapplied without the need to retranslate policy logic into enforceable controls.

Integrating Policy as Code into DevSecOps Pipelines

Implementing AI policies as code enables integration of AI governance tools with existing DevSecOps workflows. This includes:

• Policy deployment via CI/CD pipelines
• Version control via Git repositories
• Automated pre-release testing of policy effectiveness

By doing so, security and platform teams ensure more consistent enforcement of corporate AI policies within new software. Additionally, this is accomplished via automated checks, limiting manual approvals and additional friction and overhead in deployment processes.

What Is an AI Governance Framework and How Do Tools Operationalize It?

An AI governance framework is a structured model for an organization’s AI governance, including processes, roles, and principles, such as the NIST AI RMF. AI governance tools can help align policies, controls, and metrics to these frameworks to simplify regulatory compliance.

Mapping Tools to NIST AI RMF and the EU AI Act

The various capabilities of AI governance tools often map directly to key elements of AI governance frameworks. For example, automated discovery of AI usage aligns with the “governance” and “map” functions of the NIST AI RMF, while risk assessments and controls are parts of the “measure” and “manage” elements. By using these tools, organizations can more easily identify and track the AI models and use cases that fall into higher risk categories under the EU AI Act and similar regulations.

Role-Based Governance for Security, Compliance, and Business Owners

Defining accountability is a key element of any AI governance program. AI governance tools can help with defining and managing RACI (Responsible, Accountable, Consulted, and Informed)- style role definitions for key stakeholders, including:

• Security leads
• Data protection officers
• Compliance
• Application owners
• Business stakeholders

The complexity of AI governance – requiring deep technical knowledge and an understanding of risks and applicable regulations – often makes a collaborative approach superior to top-down mandates. With clearly-defined roles, organizations can build effective governance programs that leverage the strengths of all relevant parties.

How Do AI Governance Tools Integrate with Network, Security, and SASE Controls?

SASE governance tools must integrate with the rest of an organization’s security stack. They need access to identity, network, and data protection capabilities to enforce corporate policies and identify potential violations.

One of the most effective ways to implement AI governance is through deployment as part of a converged SASE platform. For example, integration of AI governance with CASB and SWG enables an organization to monitor and manage the use of AI-powered SaaS apps and GenAI chatbots.

Aligning AI Governance with SASE and SSE

A key advantage of SASE and SSE is that they centralize networking and security capabilities into a single, cloud-based platform. This combination means that all WAN traffic passes through SASE PoPs, allowing them to inspect traffic and enforce AI policies based on identity, application, and data type. As a result, the organization can manage AI governance across the entire corporate WAN from within a single solution.

Coordinating with CASB, SWG, DLP, and ZTNA

AI governance tools can integrate with and share policies and context with various tools integrated into SASE, such as CASB and SWG. This enables these tools to monitor and block content that violates corporate AI policies while feeding data to the AI governance tool as well.

AI governance tools can also combine with other elements of SASE’s converged security stack. For example, DLP can prevent data exfiltration via AI tools, and ZTNA implements least privilege access controls for AI services.

FAQs about AI Governance Tools

What is the difference between AI governance tools and AI security tools?

AI governance tools and AI security tools are both designed to help manage an organization’s use of AI and align it with security goals and compliance requirements. However, AI governance tools focus on policies, oversight, and controls across AI usage, while AI security tools focus on defending models, data, and infrastructure from attacks such as data exfiltration, model theft, and prompt injection. While these are distinct areas of focus, many platforms combine aspects of both.

Do small and mid-sized enterprises need AI governance tools?

An organization’s need for AI governance tools depends more on AI usage and risk than on the size of the business. For example, an organization with significant use of AI in a highly regulated industry or for business-critical decision-making may have a greater need than one without these factors.

Can AI governance tools help with EU AI Act compliance?

Yes, AI governance tools can help an organization to meet its compliance responsibilities under the EU AI Act. These tools can help identify an organization’s use of AI, classify the associated risk, and document the controls used to achieve compliance.

How do AI governance tools relate to existing GRC platforms?

AI governance tools complement existing GRC platforms, offering capabilities focused on addressing the challenges and risks associated with AI. This includes adding AI-specific discovery, policies, and monitoring, while GRC platforms manage broader risk and audit programs.

Are AI governance tools only for generative AI?

No, AI governance tools should address an organization’s entire exposure to AI risk, even if GenAI is often the initial focus of these efforts. Other elements of AI governance include traditional ML models, embedded AI in SaaS, and AI agents.

How Should Enterprises Evaluate AI Governance Tools?

AI governance tools have the potential to enhance an organization’s ability to monitor and manage its AI risk exposure. However, the benefits that these solutions bring depend on their embedded capabilities and deployment mode. Some key things to consider when evaluating AI solutions include:

• Coverage of AI assets and data
• Policy flexibility
• Integration with existing security stack
• Scalability
• Reporting that supports audits and regulators. 
• Alignment with recognized frameworks such as NIST AI RMF and upcoming regulations
• Support for multi-cloud and hybrid environments

AI governance tools support an organization’s governance efforts, but they are not a solution in and of themselves. They are a critical part of a broader governance, risk, and security strategy that must include policy, culture, and architecture.