What Is AI Governance?

AI governance is a set of policies, procedures, and controls designed to manage an organization’s use of AI and its exposure to associated risks. While AI is a useful technology, it carries various risks to the business, such as the potential for data breaches or AI hallucinations harming business processes.

As companies become more reliant on AI, AI governance is critical to manage security risk exposure and ensure regulatory compliance. In addition to policies and procedures, an effective AI governance program requires technical controls to enforce administrative policies and procedures.

Why AI Governance Matters for Modern Enterprises?

Most modern enterprises are exploring the potential of AI to enhance business processes. With the rise of agentic AI, this includes entrusting AI agents to operate autonomously, with little or no human oversight. This introduces significant risk to the business if AI agents make mistakes or violate corporate policies.

Additionally, the growing integration of AI into SaaS solutions means that employees may use unauthorized AI solutions for business purposes. This shadow AI creates the potential for data leakage and other AI-related threats if an organization lacks visibility into and control over this use of AI.

AI governance is essential to ensure that AI usage is aligned with the needs of the business, corporate security goals, and compliance requirements. Enterprises should define AI-specific policies for Secure Access Service Edge (SASE), Secure Web Gateway (SWG), Data Loss Prevention (DLP), Cloud Access Security Broker (CASB), and Zero Trust Network Access (ZTNA) to enforce these requirements.

What Risks Does AI Governance Help Mitigate?

AI tools can introduce various risks to the business with regard to operations, security, and other factors. Some of the most significant threats that a mature AI governance program can mitigate include:

• Inaccurate Outputs: AI tools may produce incorrect outputs or “hallucinate” due to incomplete or incorrect training data. AI governance can help to identify hallucinations and manage their effects.
• Data Leakage: AI tools with access to sensitive data may reveal it to unauthorized third parties in various ways. Data security controls are vital to manage the data accessible to AI and what it does with this information.
• Shadow AI: Unauthorized use of AI tools may move sensitive information outside of the control of the business. AI governance programs can help to detect and prevent the use of unsanctioned AI tools.
• AI Misuse: Attackers or malicious insiders with access to AI tools may use it to collect sensitive information or impair it to cause harm to the business. Monitoring and controlling inputs to AI systems can protect against prompt injection and AI misuse.

Core Functions of an AI Governance Program

An AI governance program is designed to identify and address the main risks associated with an organization’s use of AI. This includes everything from deciding what data to feed to an AI model to managing enterprise usage of AI-enabled tools to ensuring compliance with data protection regulations. Accomplishing these goals is a multi-stage, continuous process as an organization’s use of AI and its associated risks evolve, and its policies and procedures become more mature.

Policy Development and Risk Controls

Corporate AI governance policies should define policies managing the various aspects of their AI operations. Some key policies include:

• Acceptable use of AI tools
• Access control policies for AI agents
• Data handling and sharing restrictions for AI

These policies will likely evolve over time and should specify the rules for internal vs. external tools. For example, a corporate LLM that is internal to the corporate network may be allowed more access to sensitive information than ChatGPT and other third-party AI tools.

Monitoring and Usage Oversight

Monitoring and visibility into AI usage are essential for data security and regulatory compliance. If AI systems are permitted access to protected information, then the organization needs to be able to verify that this data is appropriately used and secured.

Key elements of an AI monitoring program include:

• Network tools to identify the use of sanctioned and unsanctioned AI tools
• Data security visibility tools to monitor for sensitive data flowing into and out of AI tools
• Logging AI sessions and performing reviews of AI interactions
• Reporting overall AI usage and suspicious interactions

Continuous monitoring is essential to manage the potential security threats that AI usage poses. Once a data leak has occurred or an AI agent makes a mistake in a critical workflow, it may be too late to reverse the damage.

AI Governance Implementation in Practice

AI governance is typically implemented via an iterative process, with the organization creating new policies and controls as the program matures or AI usage evolves. For example, an organization might initially define policies blocking the use of certain AI tools and later update them to permit their use with certain restrictions.

These policies should be implemented via a collaboration between security, legal, IT, and compliance teams. This ensures that policies meet the various needs of the business – protecting against cyberattacks, legal risk, and non-compliance – while also being effectively enforceable. Policies and controls should also be regularly reviewed and updated to ensure their effectiveness and to perform ongoing improvements.

Defining Approved and Restricted AI Tools

As AI is increasingly integrated into SaaS solutions and other software, enterprises need to decide which tools are permitted and which are banned. These decisions should be based on an assessment of the risk associated with the tool, balanced with the potential business benefits that it provides. For example, if an organization implements a SaaS tool on its private cloud, this represents a very different risk proposition than a similar tool managed by a third party. Internal tools may be more trusted and granted access to a wider range of sensitive data.

Even after sanctioning a particular tool, an organization should control its access. Defining onboarding processes, acceptable use, and data handling policies provides much-needed visibility and control for AI usage.

Managing Data Access and Classification Rules

Data leaks and misuse are a top risk associated with AI usage. Data may be accidentally exposed to unauthorized third parties or used in ways that put the data, the business, or its customers at risk.

Data classification and handling policies should be based on the sensitivity associated with the data. For example, an organization may ban most PII from being processed by AI but allow internal tools access to customer names and addresses. However, all external AI systems might be forbidden from accessing PII to manage data breach and regulatory noncompliance risks.

What Is an AI Governance Framework?

AI governance frameworks like the NIST AI Risk Management Framework (RMF) are designed to help organizations create policies and controls to manage their AI risk exposure. These frameworks cover key AI governance components – accountability, transparency, security, and oversight – and detail how organizations can create and mature their programs. By building programs using these frameworks, organizations can also simplify compliance by mapping regulatory requirements to controls within the framework.

Key Elements Found in Common Frameworks

AI governance frameworks may have different goals and can include a variety of elements. Some of the most significant include:

• Model Documentation: Model documentation details the model in use, its training data, and other factors to support auditability and accountability.
• Bias Evaluation: Bias evaluations attempt to identify biases that may exist in a model due to their presence in training data.
• Explainability: Explainability refers to the ability to understand how an AI model reached a decision, supporting detection of bias and errors.
• Data Controls: Data controls restrict the information available to an AI system to protect data privacy and security.
• Impact Assessments: Impact assessments determine the potential effects of deploying an AI model on the business, customers, and society to allow any harms to be managed.
• Model Drift Monitoring: Model drift monitoring works to detect changes in a model’s performance over time based on its exposure to additional data and continuous training.
• Documentation of Training: Documentation of training provides insight into how the model is created and supports auditing and compliance.
• Inference Procedures: Inference procedures explain how a model processes input to generate outputs, which is useful for evaluating reliability for production use.

How Organizations Should Evaluate AI Governance Certification Options?

AI governance certifications validate the effectiveness of an organization’s AI governance policies. These certifications may be useful for various reasons, such as regulatory compliance or building trust with customers and partners.

When evaluating AI governance certifications, key factors to consider include:

• Scope and frequency of audits
• Level of rigor
• Alignment with internal policies
• Alignment with regulatory requirements (GDPR, PCI DSS, etc.)

How AI Governance Aligns With SASE Policy Controls?

AI governance programs have limited effectiveness if policies are not enforced via technical controls. SASE architectures are ideally suited to AI policy enforcement due to their deep and comprehensive visibility across an organization’s network traffic and interaction with SaaS solutions and other programs.

Role of SWG in AI Oversight

SWGs help organizations to monitor users’ web traffic and enforce corporate security policies. These controls can be extended to support AI governance by inspecting content flowing to and from AI tools for data exfiltration, prompt injection, and other threats. This visibility into web-based resources provides critical control over the growing use of AI-enabled SaaS tools.

Role of DLP in Preventing Data Leakage

Data loss prevention (DLP) tools are designed to identify and block sharing of sensitive data with unauthorized recipients. With the rise of AI, DLP usage expands to include restricting sensitive data from being shared with AI tools or being exfiltrated by AI agents. While DLP can’t manage prompt injection and similar risks, it can reduce their impacts by controlling access to sensitive data.

Identifying Shadow AI Tools

Shadow AI is a growing threat with the proliferation of SaaS sools, browser-based apps, unauthorized browser extensions, and other unmanaged AI tools. Often, organizations without a mature AI governance program lack visibility into these solutions, exposing them to data loss and other threats. By monitoring for use of unauthorized AI apps, organizations can block or restrict their use in accordance with corporate policies.

FAQs about AI governance

What is the goal of AI governance?

AI governance is designed to manage the risks of AI usage to security, compliance, and operations. An AI governance program manages the use of AI and the data flowing into and out of it to mitigate top AI risks to the business.

What is an AI governance framework?

An AI governance framework is a tool designed to help organizations develop their own AI governance programs that meet security and compliance requirements. One example is the NIST AI Risk Management Framework (AI RMF), which details security best practices for the management of AI systems.

How does AI governance reduce Shadow AI risk?

Shadow AI is primarily a risk when an organization lacks visibility into and control over unauthorized use of AI-enabled tools. AI governance provides the ability to detect unauthorized AI usage and enforce corporate AI policies to bring AI usage into compliance with corporate policies.

Who is responsible for AI governance in an enterprise?

The responsibility for AI governance is distributed across the organization, as AI can place the business at risk in various ways. For example, AI may introduce the risk of cyberattacks, legal suits, or regulatory non-compliance. For this reason, AI governance is best implemented via a cross-functional council, including the security, IT, legal, and compliance teams, that ensures that AI policies and controls meet the needs of all parts of the business.

Is AI governance required for regulatory compliance?

While regulations may not explicitly require AI governance, it’s often implicitly required by data protection laws like GDPR and regulations such as NIS2. This is because AI tools may place sensitive data at risk of exposure, improperly use protected data, or cause failures that impact the operations of core systems. AI governance programs can help to ensure compliance with the requirements of these regulations.

AI Governance and Its Role in Secure Enterprise Operations

As companies become more reliant on AI, allowing it access to sensitive data and critical workflows, AI governance is essential. Defining and enforcing AI governance policies early helps organizations to reduce the risk of costly incidents and implement a program that can scale and mature over time.

When creating an AI governance critical, it’s vital to ensure that policies and procedures are supported and enforced by technical controls. Otherwise, Shadow AI and attackers may be able to violate or bypass policies to achieve their goals at the cost of the organization’s security and regulatory compliance.

SASE solutions are ideally suited to this, as SWG, DLP, and attack surface visibility controls can be extended to cover AI threats and solutions as well. Deploying these capabilities as part of a converged security platform with deep visibility into all WAN traffic also helps to eliminate visibility gaps and simplify management.