What Is Generative AI Security?

Generative AI (GenAI) security manages the security risks associated with LLM chatbots, such as ChatGPT or Claude. Key risks include the potential for data breaches, prompt injection, and errors generated by the LLMs. These risks are exacerbated by shadow AI, when employees use GenAI tools without corporate approval or oversight.

GenAI security is a subset of the wider field of AI security. While businesses are increasingly adopting LLMs, other applications of AI, such as predictive AI, have distinct use cases and associated security requirements.

As GenAI adoption grows, so does the need to effectively secure these systems. Corporate GenAI security programs should consider available frameworks, potential controls, and regulatory guidance to manage the risks associated with the technology.

What Falls Under the Scope of Generative AI Security?

GenAI security includes securing all aspects of GenAI usage within the business, including access management, monitoring, and acceptable use. A mature GenAI security program will include various policies and controls, including:

• Acceptable use and user behavior policies
• Prompt and data input governance
• Output review and content authenticity controls
• Access management
• Monitoring and logging of AI interactions
• Compliance and auditability for AI-related data flows
• Risk management across internal and external LLMs

A GenAI security program should consider all of an organization’s potential uses of the technology. This includes public models, internal LLMs, AI embedded in SaaS tools, browser extensions, and API integrations.

A GenAI security program touches all parts of the business, addressing cybersecurity, data protection, legal, and compliance. Programs should be developed in collaboration with all affected business units, but responsibility for the program may shift as the program and the business’s use of GenAI change and mature.

Core Concept

GenAI security includes the policies, processes, and technical controls needed to protect the organization’s use of GenAI. Assets that fall under the GenAI security umbrella include:

• Prompts
• Inputs
• Outputs
• Model access
• APIs
• Identities
• Contextual metadata
• Logs

For each of these, an organization needs to consider the “CIA triad” of confidentiality, integrity, and availability and how it affects both public LLMs and internal deployments. For example, an organization needs to protect sensitive data from being inappropriately processed by GenAI while also considering the needs of the business.

Logging and monitoring are also critical components of a GenAI program. Enterprises need visibility into GenAI usage to ensure compliance with policies and regulatory requirements and develop controls against new potential threats.

Relation to Broader AI Governance

GenAI is one component of a greater enterprise AI governance strategy. While GenAI use is widespread, companies are also adopting other forms of AI that require tailored policies and controls. However, key governance principles, such as transparency, accountability, duty of care, and risk management, apply across the board.

GenAI security’s role in the greater AI security ecosystem is defined and clarified by frameworks like the NIST AI Risk Management Framework (NIST AI RMF) and ISO/IEC 42001, which specify how to develop effective AI risk management programs. These programs are cross-function, with security teams defining controls, legal ensuring permissible use, and compliance managing audits.

Why Does Generative AI Security Matter to Enterprises Today?

GenAI usage is growing rapidly, including shadow AI, where employees use the technology without business oversight. While this brings benefits to the business, it also introduces various risks, such as the potential for breaches of sensitive customer or business data.

Without visibility into and control over corporate AI usage, an organization is vulnerable to these risks. By developing a GenAI security program, the company can manage its current AI usage and develop a program that scales with its use of the technology.

Enterprise Adoption and Exposure

Enterprise adoption of GenAI is growing rapidly, likely faster than the enterprise thinks. Many vendors are building AI into their products, meaning that existing solutions may suddenly exhibit AI-driven features.

Employees may also adopt unauthorized solutions, broadening the company’s AI exposure via shadow AI. This can happen through a variety of channels, including:

• Browser extensions
• Copy-and-pasted inputs in LLM chatbots
• AI embedded in SaaS platforms
• Unvetted APIs
• Mobile AI apps
• Unmanaged plug-ins for enterprise software

Each of these potential exposure points introduces the risk of visibility and security gaps. Without a complete picture of its AI usage, an organization can’t effectively manage the associated cybersecurity and compliance risks.

Governance and Trust Implications

AI security is a top-of-mind security consideration for businesses and consumers alike. If a company isn’t managing its AI usage, this can erode trust and introduce a variety of threats, including:

• Regulatory violations
• IP leakage
• Content authenticity issues
• Misinformation
• Loss of internal decision-making integrity

As regulations evolve, regulators increasingly expect explicit AI-related documentation and oversight. Additionally, weak GenAI governance can hinder incident response since the organization lacks visibility into unlogged data flows into and out of AI tools.

What Are the Key Generative AI Security Risks and Threats?

GenAI is a useful tool, but it’s one that comes with a variety of security risks. Key risks include data leakage, hallucinations, misuse, manipulation, and governance gaps. However, this is far from a comprehensive list, and the threat landscape is evolving with the technology and its applications.

Primary Risk Categories

While GenAI carries various risks, some are more significant than others. Some of the primary risks of GenAI include:

• Data Exfiltration: Sensitive data used in prompts and inputs may be used to train the model and displayed to unauthorized users.
• Prompt Injection: Attackers or insider threats may craft prompts to gain unauthorized access to sensitive data or cause the AI tool to take undesirable actions.
• Model Inversion: Analysis of an AI model may reveal the sensitive and personal data used to train it.
• Synthetic Content Abuse: Use of GenAI to create abusive or malicious AI-generated content.
• Shadow AI: Use of unauthorized GenAI tools within a business context.
• Model Misuse: Use of GenAI tools for fraudulent activities or as part of a cyberattack.
• Hallucinations: Incorrect outputs created by GenAI can negatively impact business workflows.

Organizational Impact

Beyond direct AI security risks, ungoverned use of GenAI can have various negative consequences for the business. Top threats include:

• Regulatory penalties
• IP loss
• Misinformation
• Brand damage
• Operational disruption

For example, an organization’s sensitive business data may be entered into a public GenAI tool, causing it to appear in another user’s output. Alternatively, reliance on GenAI for key business decisions could harm the business if the tool hallucinates. Managing these risks requires ongoing monitoring of GenAI inputs and outputs and logging sessions to support incident response activities.

Emerging Generative AI Threats

GenAI is a powerful and evolving tool that allows it to be used maliciously in various ways. Attackers are increasingly using GenAI to improve and scale phishing and other social engineering attacks and to develop novel malware variants. As GenAI models improve, this threat will escalate as the tools can generate more realistic phishing messages and deepfakes and produce higher-quality malicious code.

What Controls and Best Practices Help Manage Generative AI Security?

Enterprises can best manage GenAI security risks by controlling access to AI tools and managing the data that flows into and out of them. While organizations are working to implement governance, monitoring, access controls, and lifecycle management for GenAI, controls are varying in effectiveness due to maturity gaps, limited internal expertise, and inconsistent policy enforcement.

Visibility and Inventory

Visibility and a comprehensive inventory are the foundation of a GenAI security program because organizations can’t secure GenAI tools that they don’t know exist. Enterprises can build these inventories using endpoint logs, browser extension audits, firewall logs, and SaaS discovery tools. However, these inventories can rapidly grow stale as GenAI usage evolves, making continuous monitoring and updates a necessity.

Policy and Access Controls

With visibility into corporate GenAI usage, an organization can develop acceptable use policies and access controls to align this use with corporate security policies. Enterprises can apply zero trust principles to their GenAI security program via:

• Role-Based Access Controls (RBAC): RBAC tailors access and privileges to a user’s role in the business, simplifying provisioning and management.
• Identity Validation: Strong authentication mechanisms, such as multi-factor authentication (MFA), reduce the risk of account takeover attacks.
• Least Privilege: Least privilege access controls restrict GenAI usage to what is necessary for the user’s role in the business.

Data Protection and Monitoring

Controlling data inflows and outflows for GenAI tools is essential to managing top security risks, such as data breaches and prompt injection. Tools that organizations can use to implement data protection and monitoring for GenAI include:

• Content Filtering: Secure web gateways (SWGs), Data Loss Prevention (DLP), and similar tools can monitor traffic to web-based GenAI tools and control information included in prompts and responses.
• Prompt-Level Redaction and Classification: Organizations can inspect prompts, redacting sensitive information and applying a classification level to the prompt, before permitting or blocking the prompt.
• Prompt Logging: Logging prompts to GenAI systems can help to identify prompt injection attempts and investigate in the wake of a GenAI-enabled cyberattack.
• Anomaly Detection: Monitoring for unusual data access or actions by GenAI systems can help to detect model misuse and similar threats.

Many regulations, such as the GDPR, PCI DSS, and HIPAA, implement restrictions on the use of protected data and requirements for its security. Data protection and monitoring is essential to keep GenAI usage compliant with these requirements.

Lifecycle Governance and Compliance

GenAI security risks can manifest at any stage of their lifecycle, including evaluation, onboarding, monitoring, reviewing, and retiring AI tools. Organizations need to govern these risks at each stage, performing audits, documenting processes, and periodically updating procedures and associated controls. Maturity will grow over time, as employees grow more familiar with risks and best practices, and the organization is able to enhance, scale, and refine its controls.

How Generative AI Security Works in Practice

A GenAI security program is one element of a broader cybersecurity and data security practice. Without implementing GenAI security policies as technical controls, these policies are unenforceable and create a false sense of security. A mature GenAI security program is one where all affected business units – legal, security, IT, data science, and compliance – collaborate to define policies that meet their needs and can be effectively implemented and enforced at scale.

Integration with Broader Cybersecurity Programs

GenAI is part of a broader cybersecurity program, and many of the top GenAI threats are just a new version of existing risks. For example, data breaches are a top security concern for most businesses, and GenAI simply represents a new way for sensitive data to be leaked to unauthorized parties.

GenAI security programs are most effective and scalable when they are integrated with existing cybersecurity programs. Implementing new rules for identity management, DLP, and other aspects of GenAI security within existing tools effectively implements these policies without adding operational complexity and visibility gaps.

Future Trends and Evolving Controls

The field of GenAI security is in its infancy as the technology changes rapidly, and organizations are working to identify and address the associated risks. As a result, new solutions, such as browser isolation, AI dashboards, prompt guardrails, model access restrictions, and policy-driven prompt governance systems, are being adapted, developed, and deployed to offer greater control over GenAI threats to the business.

As GenAI matures and gains adoption, the security risks that it creates will grow as well. Adopting tailored, advanced solutions for AI threat management will be essential to maintain effective visibility and prevent potential misuse and abuse of these systems.

Summary and Outlook

GenAI security addresses the various risks associated with the use of GenAI in the business. This includes protecting data against unauthorized access and abuse, preserving trust in the business, and maintaining compliance with applicable regulations.

GenAI offers significant potential business benefits, but organizations must also work to control the risks that it brings. Key elements of an effective and scalable GenAI security strategy include comprehensive AI visibility, clear AI governance, controlled access to GenAI tools and data, and enforcement of responsible use of these technologies.

The use of GenAI and other AI tools will only grow as enterprises identify use cases for the technology. At the same time, businesses need to ensure that security keeps pace to avoid GenAI creating more problems than it solves.

FAQs about Generative AI Security

What makes generative AI security different from traditional AI security?

Generative AI (GenAI) security is a single component of a broader AI security program. While GenAI security focuses on the use of GenAI tools, traditional AI security also includes building these models and the use of non-generative AI tools, such as predictive AI. GenAI security focuses more on securing the inputs and outputs of these tools against data leaks, prompt injection, and similar threats.

What are the most common generative AI security risks?

GenAI carries various security risks, including:

• Data leakage: Sensitive data entered into public tools
• Prompt injection: Malicious prompts that attempt to extract or alter information
• Model misuse: Employees using AI systems for tasks beyond their intended scope
• Synthetic content abuse: Fake documents, impersonation attempts, deepfakes, and misinformation
• Shadow AI: Unauthorized AI usage with no oversight

With a lack of visibility, oversight, and governance, an organization can’t manage these risks. As a result, it is more exposed to cyberattacks exploiting GenAI usage and the potential for regulatory non-compliance and enforcement.

How can enterprises detect shadow AI usage?

Shadow AI is the unauthorized usage of AI tools by an organization’s employees, which introduces security risks since the company can’t monitor or secure these systems. Businesses can detect shadow AI in various ways, such as:

• Network scans to detect unknown AI endpoints
• SaaS audits to identify unsanctioned tools
• Secure web gateway logs showing AI-related traffic
• Browser extension reviews and endpoint inventories
• SaaS discovery tools in CASB/SSE solutions

These technical controls can help to identify existing shadow AI usage, but organizations can be proactive as well. Providing education and approved solutions for various tasks can help educate employees about the risks of shadow AI and offer usable alternatives.

Which frameworks guide generative AI governance?

Many organizations have developed frameworks to support GenAI governance and more general AI security governance. Some of the most significant include:

• NIST AI Risk Management Framework: Trustworthiness, accountability, and security principles.
• ISO/IEC 42001: First international standard for AI management systems.
• EU AI Act: Risk-tiered governance obligations for AI deployments in the EU.
• GDPR and sector-specific privacy laws: Requirements for lawful, auditable data handling.

These frameworks provide guidance on how to develop effective, scalable AI security programs that align with regulatory requirements. Many organizations adopt elements of these frameworks to develop GenAI and AI security programs tailored to their needs.

Is generative AI security part of zero trust?

While GenAI security isn’t part of zero trust, adopting zero trust principles can dramatically improve the effectiveness of a GenAI security program. Some ways that zero trust can be applied to GenAI security include: 

• Identity verification for anyone submitting prompts
• Role-based access to models or AI tools
• Continuous monitoring of AI interactions
• Least privilege for data inputs and model access

Adopting zero trust principles for GenAI security helps to enhance visibility and offers more granular control over AI usage. This can reduce an organization’s exposure to related risks and simplify compliance with applicable regulations.