AI Governance for Generative AI: Managing LLM and AI Agent Risks

Generative AI is changing how businesses handle customer support, internal operations, data analysis and decision-making. However, giving AI systems access to business information and operational tools also introduces risks that organisations cannot afford to ignore. A chatbot can share incorrect information, an LLM exposes confidential data, or an AI agent may perform an action without proper approval. These AI agent risks highlight the need for a structured approach to generative AI governance.

Generative AI governance services can help businesses put the right policies, security controls and human oversight in place. Ongoing AI model monitoring also helps identify performance issues and potential risks after deployment. This guide explores the key risks associated with LLMs and AI agents and explains how businesses can manage them while scaling AI responsibly. 

What Does AI Governance Mean for Generative AI?

Generative AI governance is the rules, processes, and technology used by an organisation to ensure that their AI systems are developed, deployed and used appropriately. It ensures that AI applications are controlled within acceptable limits, safeguarding sensitive data and ensuring reliable outputs.

For example, if an organisation uses an AI chatbot to provide answers to customer questions, it will need access to product information, support documentation and company policies. However, it will not be given access to sensitive staff information or provide financial information to customers.

Governance defines these boundaries. It also covers how organisations manage models, review AI-generated content, assign responsibilities and respond when something goes wrong. In practice, large language model governance focuses on controlling how LLMs handle information, generate responses and interact with connected applications.

An effective governance approach helps businesses:

• Define the use of AI that is acceptable within all departments.
• Keep information about customers and company safe.
• Define access rights to AI tools.
• Use human approvals for certain decisions.
• Monitor AI behavior and investigate any anomalies.
• Assign responsibility for AI decisions.

The aim is not to restrict innovation. It is to make AI adoption more predictable, secure and accountable.

Why Is AI Governance Important for Businesses?

As more businesses integrate their customer databases, financial systems and operations using AI, there could be greater implications if a mistake is made. If an uncontrolled AI application is used it could result in leaking sensitive information, loss of customer confidence and decisions being made that do not follow company policies.

A structured approach to generative AI governance helps businesses manage these risks through clear policies, access controls and ongoing oversight. It also supports regulatory compliance, transparent decision-making and responsible AI use as systems evolve.

Key business benefits include:

• Protect business information: Ensure there is no unauthorised access to any confidential data.
• Build customer trust: Create transparent and reliable interactions with the use of AI technology.
• Lower business risks: Detect issues before they have an impact on business processes.
• Increase accountability: Specify who is responsible for the approval, management and review of AI decisions.

For example, a financial services company using an AI assistant to summarise customer records must ensure employees can access only the information they are authorised to view.

This is why enterprise AI governance must cover not just the AI model, but also its data, users, connected systems and business processes.

What Are the Main Security Risks Associated With LLMs and AI Agents?

Being aware of the threats is the first step to developing safeguards. While both LLMs and AI agents make use of comparable technology, the possible consequences may be different for each.

A. Common LLM security risks

Large language models generate responses based on their training and the information provided to them. However, they do not always understand whether information is accurate, safe or appropriate to share.

The main LLM security risks include:

Risk	What it means	Business example
Prompt injection	Malicious instructions manipulate the model’s behaviour	A chatbot follows hidden instructions in an uploaded document
Data leakage	The model reveals confidential information	An assistant shares restricted company information
Hallucinations	The model produces convincing but incorrect information	A chatbot provides incorrect pricing or policy details
Data poisoning	Manipulated training or reference data affects outputs	An internal knowledge assistant retrieves misleading information
Uncontrolled usage	Excessive requests increase costs or affect availability	A public AI application receives repeated automated requests

Another important concern is LLM data privacy. Businesses must understand what information enters an AI application, where it is processed, how it is stored and whether third-party providers use it for other purposes.

B. What are the risks associated with AI agents?

In addition, AI agents introduce an extra layer of risk because they can manipulate tools and systems and perform activities beyond generating responses.

For example, take the case of an AI agent that is tasked with handling customer support queries. They may be tasked with gathering order details, updating customer details and starting off the refund process.

Common risks associated with AI agents include:

• Excessive permissions: Agents receive access to systems or information they do not need.
• Unauthorised actions: An agent sends messages, changes records or initiates transactions without approval.
• Manipulated instructions: Malicious content redirects an agent towards unintended actions.
• Poor decision-making: An agent misunderstands a request and takes the wrong action.
• Limited traceability: Businesses struggle to understand why an agent performed a particular task.

These concerns make AI agent security particularly important when autonomous systems interact with financial platforms, customer databases or internal business applications.

What Are the Key Components of an AI Governance Framework?

An effective AI governance framework brings business policies, technical safeguards and human responsibilities together. It should cover the entire AI lifecycle, from selecting a use case to monitoring the application after deployment.

The following components provide a practical starting point

Component	What businesses should establish
Data governance	Rules for collecting, storing and using business information
Access management	Role-based permissions for employees and AI agents
Human oversight	Approval processes for sensitive decisions and actions
Security controls	Protection against malicious inputs and unauthorised access
Transparency	Clear information about AI use and generated content
Monitoring	Regular checks of AI outputs, behaviour and system activity
Accountability	Defined ownership for AI systems and their outcomes

This should also be reflected through your AI governance policies. You should be clear on what tools your staff can use, what data can be uploaded and when a manager’s approval is required.

Within an organisation that has multiple AI applications in use, enterprise AI governance will ensure consistency across departments but also allow you to set appropriate controls based on the risk of the use cases.

Similarly, large language model governance should cover model selection, prompt management, data access, output validation and third-party dependencies.

For AI agents, the rules should be even more specific. Define which tools an agent can access, which actions it can perform independently and which activities require human approval. Strong AI agent security depends on limiting permissions rather than assuming the agent will always follow its instructions.

How Can Organisations Ensure Safe AI Adoption?

Start with the AI use cases you plan to use within your business and understand the risks associated with them. Ensure the controls you put in place are focused on doing what they need to do.

Here are six practical steps.

Step 1: Identify AI Use Cases

List the areas where you are already using AI, and any new areas you wish to use AI.

Examples: Chatbots for Customer Support, Document Processing, Recruitment Tools, Knowledge Assistants, Autonomous Agents.

What does it do? Who uses it? What data is required from your business?

Step 2: Assess Potential Risks

Consider what would happen if your AI application were incorrect, showed sensitive data or did something you didn’t expect.

Having a system to control the risk of your AI applications will be important. Group your applications by risk and ensure that the ones with a higher risk have the right controls in place.

Step 3: Establish Clear Usage Policies

Ensure that your AI policies are clear about how AI can be used within your organisation, including any restrictions on data sharing. Ensure your employees understand what they can and cannot do.

For example, you may require your employees to only use an approved AI assistant to summarise public documents. However, they would need permission to upload confidential client information.

Step 4: Introduce Access Controls

Limit access to the information and tools that your AI applications can use. 

Define precisely what your AI agents can do, such as send emails, update records and process transactions. Require human authorisation for any sensitive or irrevocable tasks.

Step 5: Test Before Deployment

Run tests with real-world scenarios. Test how your AI application handles ambiguous, sensitive, incorrect and unexpected requests.

Also ensure that your testing is thorough and checks if your security controls are working.

Step 6: Monitor and Improve

Once deployed, governance should continue. This can be achieved by monitoring the outputs of your AI models to analyse quality, anomalies, access and performance.

It is important to document key decisions, look into any incidents and ensure that the controls are kept up to date as the business requirements change.

This ensures that implementing AI responsibly becomes an embedded process within the business.

What Controls Should Businesses Implement Before Deploying AI Agents?

Before giving an AI agent access to business applications, decision-makers should establish clear operational boundaries.

The following controls are particularly important:

• Access control through role-based security: Give the agents access only to the systems and files that they need to carry out their duties.
• Human authentication: Ensure that there is human approval for activities like money transfer, refunding clients, and communication with the outside world.
• Controls on activities: Limit the number of transactions, requests, and actions that the agent is allowed to undertake.
• Recording of activities: Record all instructions issued by the agent, tools used, and actions undertaken.
• Validation of input: Validate the input given by users, external documents, and applications connected.
• Emergency controls: Provide ways to disable or shut down the agent.

Which AI Governance Frameworks and Standards Should Businesses Know?

Businesses do not need to create every governance rule from scratch. Established frameworks and standards provide useful guidance for managing AI risks.

Here are three worth understanding:

Framework	How it helps
NIST AI Risk Management Framework	Provides a structured approach to identifying, assessing and managing AI risks
ISO/IEC 42001	Sets requirements for establishing and continually improving an AI management system
OWASP GenAI Security Guidance	Helps development teams understand and address LLM and AI agent security vulnerabilities

The NIST Generative AI Profile provides an extension to the NIST AI risk management framework, providing guidance for addressing the unique risks of Generative AI. There are four key functions: Govern, Map, Measure and Manage.

ISO/IEC 42001 details requirements for building, maintaining and improving an AI management system. It will be relevant to businesses that design, deliver or use AI systems.

Businesses within the UK and US should also consider any relevant privacy and data protection requirements, and sector specific requirements. Organisations in the EU should also consider what duties they have under the EU AI Act based on their role and the use of the AI system.

Common AI Governance Mistakes Businesses Should Avoid

Even businesses with established IT and security processes can overlook risks when introducing Generative AI.

Watch out for these common mistakes:

• Governance is seen as a single process: AI tools evolve and, thus, controls require periodic assessments.
• Granting too many privileges to agents: The unnecessary privileges increase the level of possible damage.
• Neglecting employees’ use of AI: Unauthorised software can reveal company secrets.
• Dependence on AI solutions alone: Information and decisions still need to be validated.
• Failure to recognise third-party vendors: Companies have to know how the third-party AI systems deal with their data.
• Not providing an owner for AI systems: Every AI tool requires a certain owner.

Avoiding these mistakes makes responsible AI implementation easier to manage as adoption expands across the organisation.

How Does AI Governance Support Long-Term Business Growth?

Good governance is not simply about preventing problems. It gives businesses a more reliable way to introduce AI into important workflows.

With clear controls, organisations can expand AI usage across customer service, finance, operations and internal knowledge management without losing sight of security and accountability.

A mature AI risk management approach also helps leadership make better investment decisions. Teams can identify which applications are suitable for automation, which need additional testing and where human involvement remains essential.

Over time, this creates a stronger foundation for innovation, operational efficiency and customer confidence.

Ending Note 

Generative AI offers significant opportunities, but businesses need clear rules and technical safeguards to manage its risks. From protecting sensitive information to controlling autonomous agents, governance should remain part of every stage of AI development and deployment.

The right approach helps organisations adopt AI confidently while maintaining control over their data, systems and business decisions.

With expertise in AI consulting and integration, IIH Global provides AI security and governance services to help businesses address the challenges of adopting generative AI.

If you are looking to hire AI developers to build and integrate AI applications, talk to IIH Global’s AI experts to discuss your project.