AI Risk and Security

Introduction

AI can help businesses automate work, analyze information, support employees, and improve customer experiences. However, introducing AI also introduces new risks.

An AI system can expose sensitive information, generate incorrect information, follow an untrusted instruction, misuse a connected tool, or make an inappropriate recommendation. These risks become more important when AI is connected to business systems or allowed to take actions automatically.

AI risk and security therefore need to be considered before deployment and throughout the life of the system.

The goal is not to eliminate every possible risk. The goal is to understand the risks, reduce them to an acceptable level, and establish controls that limit the impact when failures occur.

What Is AI Risk?

AI risk refers to the possibility that an AI system, its outputs, its use, or its surrounding processes could cause unwanted consequences.

Risks can involve:

• Security.
• Privacy.
• Accuracy.
• Reliability.
• Financial loss.
• Operational disruption.
• Reputation.
• Legal or contractual obligations.
• Unfair or inappropriate outcomes.
• Unauthorized actions.

The level of risk depends on what the AI system does, what information it processes, who is affected, and what happens when it makes a mistake.

AI Risk Is Not Just Model Risk

A common mistake is to think of AI security as simply choosing a secure AI model.

An AI system usually consists of more than a model. It may include applications, prompts, data sources, APIs, databases, user accounts, external services, business rules, and connected tools.

A security problem can occur in any of these components.

For example, a well-protected AI model can still create a serious security problem if the application gives it unrestricted access to a sensitive database.

Major Categories of AI Risk

Businesses can organize AI risks into several broad categories.

1. Data Risk

AI systems may process confidential, personal, financial, operational, or proprietary information.

Problems can occur when:

• Unauthorized information is submitted to an AI service.
• Data is stored longer than intended.
• Access permissions are too broad.
• Information is sent to an inappropriate third-party service.
• Sensitive information appears in generated outputs.

2. Output Risk

AI systems can produce incorrect, incomplete, outdated, or misleading information.

This can be especially dangerous when users assume that fluent language means accurate information.

3. Security Risk

AI applications can be targeted by attackers. Attackers may attempt to manipulate prompts, exploit connected tools, access sensitive information, or cause unintended actions.

4. Automation Risk

The consequences of an AI error become greater when the system can act automatically.

A wrong draft may be easy to correct. An AI system that automatically changes customer records or sends financial instructions can create much greater impact.

5. Third-Party Risk

Businesses often depend on external AI providers, APIs, cloud services, and software platforms. Problems with a provider can affect the business using the service.

Confidential Data and AI

Employees may be tempted to paste information into an AI assistant because it is convenient.

Examples of sensitive information can include:

• Customer records.
• Employee information.
• Financial information.
• Passwords and authentication secrets.
• Private contracts.
• Business strategies.
• Source code.
• Proprietary research.

Organizations should define which information can be processed by approved AI systems and under what conditions.

Employees should never assume that an AI service handles information in the same way as an internal business system.

Data Minimization

One useful security principle is data minimization.

The idea is simple: provide only the information required for the task.

Suppose an AI system needs to classify customer support requests. It may not need the customer full address, payment information, or other unrelated personal details.

Reducing unnecessary information can reduce the potential impact of a data exposure.

Access Control

AI applications should have appropriate access controls.

Access control determines who or what can access a resource.

For an AI system, this can include:

• Which employees can use the system.
• Which data sources it can access.
• Which tools it can call.
• Which actions it can perform.
• Which environments it can access.

Access should be granted according to business need.

Least Privilege

The principle of least privilege means giving an AI system only the permissions required to perform its approved tasks.

For example, an AI assistant that reads support tickets may need permission to retrieve ticket information. It may not need permission to delete customer records.

Limiting permissions reduces the potential damage caused by errors, misuse, or compromise.

Authentication

Authentication verifies the identity of a user or system.

AI applications should not rely on anonymous access when sensitive information or important actions are involved.

Appropriate authentication and account controls help ensure that only authorized users and systems can interact with protected resources.

Authorization

Authentication answers the question, "Who are you?"

Authorization answers the question, "What are you allowed to do?"

Both matter for AI systems.

An employee may be authorized to use an internal AI assistant but not authorized to access every document available to the organization.

The AI system should respect the same or stronger access boundaries rather than becoming a way around existing permissions.

AI Hallucinations and Incorrect Outputs

AI systems can sometimes generate information that sounds convincing but is incorrect.

This creates an important business risk.

Incorrect output can result in:

• Wrong customer information.
• Incorrect internal recommendations.
• Faulty reports.
• Incorrect financial analysis.
• Misleading marketing material.
• Incorrect operational decisions.

The appropriate response is not simply to stop using AI. Instead, the organization should determine where verification is required.

Human Verification

Human review can act as an important control for higher-impact AI outputs.

The reviewer should have enough knowledge and authority to evaluate the result.

For example, an AI system may draft a customer response, while an employee verifies important claims before the response is sent.

For a low-risk brainstorming task, extensive review may not be necessary. For a high-impact decision, stronger review may be required.

Prompt Injection

AI systems can receive instructions from users, documents, websites, emails, or other external sources.

A malicious or untrusted source may contain instructions designed to influence the AI system in an unintended way. This type of attack is commonly called prompt injection.

For example, an AI assistant may be asked to summarize an external document. The document could contain text attempting to instruct the AI assistant to ignore its original task or reveal information.

The important lesson is that information retrieved by an AI system should not automatically be treated as trusted instructions.

Untrusted Content

AI applications often combine trusted instructions with information retrieved from external or user-provided sources.

Applications should distinguish between:

• Instructions that define system behavior.
• Business rules.
• User requests.
• External information.
• Untrusted content.

This distinction becomes particularly important for AI agents and applications that can use tools.

Tool and API Security

An AI application may connect to tools such as:

• Email systems.
• Customer relationship systems.
• Databases.
• Calendars.
• File storage.
• Payment systems.
• Business APIs.

Every connection creates another security boundary that needs protection.

Credentials should be securely managed, permissions should be limited, and important actions should have appropriate safeguards.

AI Actions Should Be Controlled

There is a major difference between AI that provides information and AI that can take actions.

Consider three levels:

As the AI system gains more authority, security and governance requirements generally increase.

Guardrails

Guardrails are controls that constrain AI behavior or reduce the impact of mistakes.

Examples include:

• Allowed and prohibited actions.
• Input validation.
• Output validation.
• Access restrictions.
• Human approval.
• Rate limits.
• Transaction limits.
• Restricted tool permissions.
• Logging.
• Automatic stopping conditions.

Guardrails should be designed around realistic failure scenarios rather than added only as generic warnings.

Logging and Audit Trails

Logs can help organizations understand how an AI system was used and what happened during an incident.

Depending on the system, useful records may include:

• User identity.
• Time of activity.
• System or workflow used.
• Tools called.
• Actions performed.
• Errors.
• Approval events.
• Relevant system decisions or outcomes.

Logging must itself be designed carefully because logs can contain sensitive information.

Monitoring

Security and risk monitoring should continue after deployment.

Organizations can monitor for:

• Unusual access patterns.
• Unexpected tool usage.
• Repeated failed requests.
• Large volumes of automated activity.
• Unexpected data transfers.
• Sudden changes in output quality.
• Policy violations.
• Security incidents.

Monitoring helps detect problems that were not discovered during testing.

Third-Party AI Security

External AI providers can introduce additional risks.

Before adopting an important third-party AI service, organizations should consider:

• How information is transmitted.
• How information is stored.
• How long information is retained.
• Who can access it.
• What security controls are available.
• How incidents are handled.
• What happens if the provider becomes unavailable.
• What contractual commitments exist.

The exact evaluation should depend on the sensitivity and importance of the use case.

Model and Provider Dependency

AI applications may become dependent on a particular model provider or service.

Organizations should consider what happens if:

• The provider changes pricing.
• The model is replaced.
• The service becomes unavailable.
• Performance changes.
• Terms change.
• A critical API is discontinued.

Important business systems should have an appropriate continuity plan.

AI Risk Assessment

A practical risk assessment can follow a simple sequence.

1. Identify the AI system: understand what it does and where it is used.
2. Identify assets: determine what data, systems, and business processes are involved.
3. Identify threats: consider accidental errors, malicious attacks, misuse, and system failures.
4. Estimate impact: determine what could happen if the risk occurs.
5. Estimate likelihood: consider how likely the event is to occur.
6. Define controls: introduce measures that reduce likelihood or impact.
7. Test controls: verify that the controls actually work.
8. Monitor: continue watching for changes and new risks.

Risk Matrix

A simple risk matrix can help prioritize attention.

This is only a starting point. Organizations should use more detailed assessment methods when the potential impact is significant.

Security Testing

AI applications should be tested against realistic misuse and failure scenarios.

Testing can include:

• Unexpected user inputs.
• Attempts to access restricted information.
• Malicious instructions.
• Untrusted documents.
• Unauthorized tool requests.
• Incorrect or incomplete data.
• Repeated automated requests.
• Attempts to bypass business rules.

The objective is to discover weaknesses before attackers or normal users discover them in production.

Secure AI Development

For organizations building AI applications, security should be included throughout development.

Important practices include:

• Secure authentication.
• Strong authorization.
• Secret management.
• Input validation.
• Output validation.
• Dependency management.
• Secure API design.
• Logging and monitoring.
• Testing before deployment.
• Regular security review.

AI-specific risks should be considered alongside traditional application security.

Business Continuity

AI may become part of important business processes. Organizations should consider what happens when the AI service is unavailable or produces unacceptable results.

Possible controls include:

• Manual fallback procedures.
• Alternative service providers.
• Rate and usage limits.
• Backup processes.
• Defined shutdown procedures.
• Human escalation paths.

A critical process should not depend on an AI service without considering how the business will operate if that service fails.

Common AI Security Mistakes

Giving AI Too Much Access

Broad permissions increase the potential impact of errors or compromise.

Trusting Every AI Output

AI-generated information should be evaluated according to the importance of the task.

Ignoring Untrusted Instructions

Information retrieved from documents, websites, or messages may contain instructions that should not be treated as trusted system instructions.

Skipping Human Approval

High-impact automated actions may require explicit human authorization.

Failing to Monitor

Security controls and AI behavior should be monitored after deployment.

Ignoring Third-Party Risk

An external provider can become an important part of the organizations risk profile.

A Practical AI Security Framework

A business can use the following framework when introducing an AI system:

1. Understand: document the purpose, users, data, model, tools, and actions.
2. Classify: determine the potential risk and business impact.
3. Restrict: apply least privilege and appropriate access controls.
4. Protect: secure data, credentials, APIs, and connected systems.
5. Validate: test inputs, outputs, workflows, and failure scenarios.
6. Review: require human approval where appropriate.
7. Monitor: track security, performance, usage, and unusual behavior.
8. Respond: establish procedures for incidents and failures.
9. Improve: update controls as the system and threat environment change.

Conclusion

AI risk and security are essential parts of responsible business AI adoption.

Organizations need to consider more than the security of an AI model. They must also protect data, manage permissions, secure connected systems, validate outputs, control automated actions, evaluate third-party providers, monitor activity, and prepare for incidents.

The principle of least privilege is especially important for AI systems with access to tools and business systems. The system should have enough authority to perform its approved function, but no more.

Strong AI security does not prevent businesses from benefiting from AI. It creates the controls needed to use AI confidently while reducing the impact of mistakes, misuse, and attacks.