What should a Gen AI risk awareness program include?

Your definition of an "insider" must now expand to include AI agents and other non-human actors operating within your systems. These agents often have the same access as employees, creating a new layer of risk that traditional security tools were not built to see. Managing this blended human-machine workforce requires a unified strategy that provides visibility into the actions of every actor. This raises a critical question for security leaders: what should a Gen AI risk awareness program include? A truly effective program must extend visibility to these AI agents, monitoring the growing intersection of human and machine-driven risk. Living Security, a leader in Human Risk Management (HRM), provides this unified view by analyzing data across behavior, identity, and threats for all actors.

Key Takeaways

• Unify Risk Signals for Predictive Insight: Go beyond simple monitoring by correlating data across behavior, identity, and threat intelligence. This approach provides a complete view of your Gen AI risk landscape, allowing you to predict and prioritize vulnerabilities tied to both human and machine actors.
• Operationalize AI Governance with Targeted Training: Establish clear acceptable use policies and a cross-functional governance committee to create guardrails for AI use. Reinforce these rules with adaptive, role-specific training and AI-powered phishing simulations to build a security-first culture.
• Measure and Adapt for Lasting Resilience: Prove your program's value by tracking key risk indicators and demonstrating a reduction in risky behaviors. Use AI with human oversight to enforce policies at scale and continuously adapt your strategy to stay ahead of new threats and regulations.

What Are the Core Risks of Generative AI?

The rapid adoption of generative AI tools brings incredible opportunities for productivity and innovation. However, it also introduces a new and complex set of risks that organizations must manage proactively. These are not just technical vulnerabilities; they are fundamentally human risks, stemming from how employees interact with this powerful new technology. From accidentally leaking sensitive data to falling for AI-powered social engineering schemes, the human element is central to nearly every generative AI threat.

Understanding these core risks is the first step toward building an effective awareness program and a resilient security posture. A modern Human Risk Management (HRM) strategy must evolve to address these emerging threats. By correlating signals across employee behavior, identity systems, and real-time threat intelligence, security leaders can gain the visibility needed to predict and prevent incidents before they happen. Let's explore the specific challenges generative AI presents to your enterprise.

Data Privacy and Leakage

One of the most immediate risks of generative AI is the potential for data leakage. When employees use public AI models, they might input sensitive information, such as customer data, financial records, or confidential project details, to get help with their tasks. This information can be absorbed into the model's training data, making it potentially accessible to others outside your organization. This creates a significant data privacy and security issue. Without clear policies and technical controls, your proprietary information could be exposed through an employee's simple copy-and-paste action, leading to compliance violations and loss of competitive advantage.

Prompt Injection and AI-Generated Malware

Attackers are already exploiting generative AI through techniques like prompt injection. This involves crafting special instructions that trick an AI model into ignoring its safety protocols, potentially causing it to reveal sensitive data or execute malicious commands. On the other side of the coin, threat actors are using generative AI to create highly convincing phishing emails and novel malware strains at an unprecedented scale. This makes it much harder for employees to identify threats, as traditional red flags may no longer apply. Your security team needs advanced tools, like AI-tailored phishing simulations, to prepare employees for this new generation of attacks.

Misinformation and AI Hallucinations

Generative AI models can sometimes produce "hallucinations," which are outputs that sound confident and factual but are completely incorrect. An employee might unknowingly use this fabricated information in a critical report, a financial projection, or a marketing campaign. Relying on inaccurate, AI-generated data can lead to poor business decisions, damage your company's reputation, and even create legal liabilities. A key part of your security awareness program should be training employees to critically evaluate and verify any information they get from AI tools, treating it as a helpful starting point, not an absolute truth.

Deepfakes and AI-Driven Social Engineering

The rise of generative AI has made creating hyper-realistic deepfakes, or fake audio and video content, easier than ever. This technology supercharges social engineering attacks. Imagine a threat actor using a deepfake to impersonate your CEO's voice in a phone call, instructing an employee in finance to make an urgent wire transfer. These impersonation attacks are incredibly difficult to detect because they exploit human trust. Educating your workforce about the existence of deepfakes and establishing strict, multi-channel verification processes for sensitive requests is essential to counter this growing threat.

Intellectual Property Exposure

Your company's intellectual property (IP) is one of its most valuable assets, and generative AI introduces a new vector for its exposure. Employees, especially developers and engineers, might be tempted to paste proprietary source code or product designs into a public AI tool to debug it or generate new ideas. Once that data is entered, you lose control over it. It could be used to train the model and inadvertently shared with other users, including your competitors. Protecting your IP requires clear acceptable use policies and providing employees with secure, company-sanctioned AI tools that keep your data private.

Shadow AI and Unsanctioned Tool Use

"Shadow AI" refers to the use of AI applications by employees without the knowledge or approval of your IT and security teams. Driven by a desire for efficiency, employees may sign up for dozens of different AI tools using their work credentials, creating a massive blind spot for your organization. You can't protect what you can't see. This unsanctioned tool use makes it impossible to enforce data handling policies or manage risk effectively. A comprehensive HRM platform can help you gain visibility into this activity by analyzing behavior and identity signals to see which tools employees are using and assess the associated risks.

How to Assess Your Gen AI Risk Landscape

Before you can manage the risks of generative AI, you have to make them visible. Flying blind is not a strategy. A thorough assessment turns abstract fears about AI into a measurable and actionable risk landscape, which is a core principle of an effective Human Risk Management (HRM) program. This process isn't just about scanning for new tools; it’s about understanding the complex interactions between your employees, the AI systems they use, and the data they access.

A proactive assessment allows you to move from a reactive posture, where you only respond after an incident, to a predictive one. By systematically evaluating your organization's exposure, you can pinpoint the most critical vulnerabilities before they are exploited. This involves mapping the flow of data, identifying which roles and systems are most exposed, building a comprehensive risk inventory with input from across the organization, and leveraging established frameworks to guide your efforts. Taking these steps provides the data-driven foundation needed to build a targeted and effective Gen AI risk awareness program.

Map Behavior, Identity, and Threat Signals

To truly understand your Gen AI risk, you need to see the full picture. A single data point, like an employee using a public AI tool, is only a small part of the story. A comprehensive assessment requires correlating signals across three critical pillars: behavior, identity, and threat. This approach allows you to connect the dots between what people are doing, who they are, and the dangers they face.

For example, you can analyze behavioral data to see which AI tools are being used and what kind of information is being shared. Then, you can layer on identity and access data to see if the employees using these tools have access to sensitive intellectual property or customer data. Finally, you can integrate threat intelligence to see if those same high-access employees are being targeted by AI-driven phishing campaigns. The Living Security platform is built to analyze these signals, giving you a unified view of risk.

Identify High-Risk Roles and Access Points

Gen AI risk is not distributed evenly across your organization. Some employees, by the nature of their roles, represent a much higher level of risk. A developer with access to proprietary source code, a marketer working with confidential launch plans, or a member of your legal team handling sensitive case files all pose a significant risk if they inadvertently expose that data to a public AI model.

Identifying these high-risk roles and access points is a critical step in prioritizing your defensive efforts. By analyzing who has access to what, you can pinpoint the individuals and departments that need the most support and guidance. This allows you to move beyond generic, one-size-fits-all training and instead provide targeted interventions for the people who need them most. This proactive approach helps you address vulnerabilities before an employee accidentally shares private company information.

Build a Cross-Departmental Risk Inventory

Security teams cannot and should not assess Gen AI risk in a vacuum. The implications of this technology stretch across the entire enterprise, touching everything from legal and compliance to product development and marketing. To build a truly comprehensive risk inventory, you must get input from a wide range of stakeholders, including leaders from IT, legal, and various business units.

This collaborative process ensures all facets of risk are considered. Your legal team can identify potential intellectual property and copyright issues, while business leaders can highlight how AI is being used to drive operational goals. This cross-departmental effort not only creates a more accurate risk picture but also fosters a shared sense of ownership and accountability. The Human Risk Management Toolkit can provide a valuable starting point for organizing this cross-functional initiative and building consensus.

Adopt Frameworks Like the NIST AI RMF

You don’t need to reinvent the wheel when building your Gen AI risk assessment process. Several established frameworks can provide a structured and defensible methodology. One of the most recognized is the NIST AI Risk Management Framework (AI RMF), which offers a voluntary but comprehensive guide for managing risks associated with artificial intelligence.

The framework helps organizations govern, map, measure, and manage AI risks throughout the technology's lifecycle. Adopting a framework like the NIST AI RMF brings a level of rigor and standardization to your program, ensuring your approach is aligned with industry best practices. It provides a common language and a clear set of steps for identifying, analyzing, and responding to risks, which is essential for building a mature and effective program. This commitment to established standards also demonstrates due diligence to regulators, partners, and your board.

What Are the Key Components of a Gen AI Risk Awareness Program?

Adopting generative AI without a clear strategy is like navigating a minefield blindfolded. To use these powerful tools safely, you need a structured program that makes your teams aware of the risks and guides them toward secure practices. A robust Gen AI risk awareness program is a critical part of any modern Human Risk Management strategy. It moves your organization from a reactive posture to a proactive one, preventing incidents before they happen. The program should be built on four key pillars: a risk framework, clear usage policies, a defined governance structure, and a dedicated incident response plan. Let's look at what each of these components involves.

Risk Identification and Classification Framework

You cannot manage risks you cannot see. The first step is to build a framework for identifying and classifying potential Gen AI threats. As experts at Wolters Kluwer note, "Companies need to understand these risks and put plans in place to manage them right now." This means systematically mapping out dangers like data leakage, prompt injection, and intellectual property exposure. By correlating signals across employee behavior, identity systems, and threat intelligence, you can create a comprehensive view of your unique risk landscape. This data-driven approach helps you prioritize the most urgent threats and focus your resources where they will have the greatest impact, forming the foundation of an effective program.

Clear Policies and Acceptable Use Guidelines

Once you identify the risks, you need to establish clear rules of the road for your employees. According to Deloitte Insights, companies must "set up clear rules for how AI is used." An Acceptable Use Policy (AUP) for generative AI is non-negotiable. This document should explicitly state which AI tools are approved for use, what types of company data can (and cannot) be entered into them, and the proper procedures for handling AI-generated content. The goal is not to block innovation but to create safe guardrails that allow employees to experiment productively. A clear policy empowers your team by removing ambiguity and providing a single source of truth for secure AI interaction.

Governance Structure and Accountability

A policy is only as strong as the framework that supports it. To ensure your guidelines are followed, you must establish a clear governance structure with defined roles and responsibilities. This often involves creating a cross-functional AI governance committee, including leaders from security, legal, IT, and business units. This group is responsible for vetting and approving new AI tools, regularly reviewing policies, and holding individuals and teams accountable for compliance. As Wolters Kluwer advises, you should "clearly state who is responsible for AI decisions." This structure ensures that the oversight of AI does not fall through the cracks and that your organization maintains control over its AI ecosystem.

Incident Response Plan for AI-Driven Threats

Even with strong preventative measures, you must prepare for the possibility of an AI-related security incident. Your existing incident response plan may not be sufficient for the unique challenges posed by generative AI. You need a specific plan that outlines how to detect, contain, and remediate threats like AI-generated malware or deepfake-driven social engineering attacks. This plan should define the roles of your security, legal, and communications teams during an AI-driven crisis. The leading Human Risk Management platform can help by providing predictive intelligence to spot anomalous activity early, giving your team a critical head start in mitigating a potential incident before it escalates.

Extend Risk Visibility to AI Agents and Non-Human Actors

As your organization adopts AI, your definition of an "insider" must expand. AI agents and other non-human actors now operate within your systems, often with the same access and permissions as your employees. This shift requires a new approach to risk management, one that looks beyond human behavior to include the actions of these autonomous tools. A reactive stance is no longer enough; you need to predict and prevent incidents by understanding the complete picture of human and machine interaction.

Living Security, a leader in Human Risk Management (HRM), provides the visibility needed to manage this new, blended workforce. The leading Human Risk Management Platform is the first to be AI-native, built to help organizations predict and prevent security incidents driven by both human and emerging AI-based activity.

Why AI Agents Introduce a New Risk Layer

Generative AI is a powerful tool for productivity, but it also introduces a complex and unfamiliar risk layer. AI agents can access sensitive data, execute commands, and interact with other systems, all while acting on behalf of a human user. This creates significant new vulnerabilities. An agent could be manipulated through prompt injection, misinterpret a command leading to data leakage, or become a target for attackers seeking to exploit its permissions.

These are not just theoretical problems. As businesses integrate these tools more deeply, the Generative AI risks become a critical concern for security teams. Without visibility into how these agents are operating, you are effectively managing a large part of your workforce with a blindfold on, unable to spot anomalous activity until after an incident occurs.

Monitor Human and Machine-Driven Risk Together

Treating human and AI agent risk in separate silos is a recipe for failure. The two are deeply interconnected. An employee might use an unsanctioned AI tool, creating a "shadow AI" problem, or a compromised AI agent might act on credentials stolen from a human user. To effectively manage this new landscape, you need a unified view that correlates signals across all actors, both human and machine.

This is where a modern HRM strategy becomes essential. By analyzing data across employee behavior, identity and access systems, and real-time threat intelligence, you can see the full context of an action. The Living Security platform extends this visibility to AI agents, helping you monitor the growing intersection of human and machine-driven risk. This allows you to identify whether a risky action was initiated by a person or an autonomous system, so you can apply the right intervention and proactively reduce risk across your entire organization.

What Training Belongs in a Gen AI Risk Awareness Program?

A generic, one-size-fits-all training program is no match for the dynamic risks introduced by generative AI. To effectively reduce human-driven incidents in the age of AI, your awareness program must be targeted, adaptive, and data-driven. It’s not just about telling employees what not to do; it's about providing the right guidance to the right people at the right time. This requires a shift from broad-stroke annual training to a precise, continuous model that addresses specific vulnerabilities.

The most effective programs are built on a deep understanding of your unique risk landscape, correlating data across employee behavior, identity systems, and real-time threats. By analyzing these signals, you can identify which individuals and roles are most likely to introduce risk and what specific behaviors need intervention. This allows you to move beyond simple awareness and deliver targeted security awareness and training that actively changes behavior and strengthens your security posture against sophisticated, AI-driven threats. The following components are essential for building a training program that prepares your workforce for the realities of generative AI.

Deliver Role-Based Training for High-Risk Employees

Not all employees face the same level of Gen AI risk. A software developer using an AI code assistant has a different risk profile than a marketer using AI for content creation or an executive using it for market analysis. Your training must reflect these differences. Start by using data from your identity, behavior, and threat systems to pinpoint high-risk roles and individuals. From there, you can deliver role-based training that addresses the specific tools and scenarios they encounter daily. For example, developers may need training on preventing intellectual property leakage into AI models, while your legal team needs guidance on the compliance risks of unsanctioned AI tools. This targeted approach ensures training is relevant and actionable, making it far more effective than a generic module.

Use Micro-Training and Adaptive Learning Techniques

The days of the mandatory, hour-long annual training video are over. To keep pace with the rapid evolution of AI, you need a more agile approach. Micro-training delivers short, focused learning modules at the moment of need. For instance, if an employee attempts to paste sensitive data into a public AI chatbot, an automated system can immediately trigger a two-minute video explaining the risk and guiding them to a sanctioned tool. This is the core of adaptive learning: using real-time behavior to deliver just-in-time guidance. The Living Security platform excels at this, using its AI guide, Livvy, to autonomously orchestrate these interventions, turning risky moments into valuable learning opportunities without overwhelming your team.

Run AI-Tailored Phishing and Social Engineering Simulations

Generative AI has supercharged phishing and social engineering attacks. Malicious actors can now create flawless, highly personalized emails, messages, and even deepfake audio or video with ease. Your defense must evolve accordingly. Standard phishing simulations are no longer enough. You need to run AI-tailored phishing simulations that mimic the sophisticated, context-aware attacks your employees will face. These simulations should test their ability to recognize AI-generated content, verify identities in a zero-trust world, and report suspicious activity. The goal is not to trick employees, but to train them to recognize the subtle cues of advanced threats and build the critical thinking skills needed to operate securely.

Build a Security-First Culture for AI Use

Ultimately, technology and training are only as effective as the culture that supports them. Building a security-first culture for AI use is a critical component of any risk awareness program. This starts with clear policies and acceptable use guidelines that are communicated from the top down. Employees need to know what tools are sanctioned, what data is permissible to use, and who to contact when they have questions. As experts at Wolters Kluwer note, Enterprise Risk Management teams are essential in helping companies use GenAI safely. This creates a foundation of shared responsibility where everyone understands their role in protecting the organization. A strong security culture empowers employees to innovate with AI confidently while making safe, informed decisions.

How to Stay Compliant with Evolving Gen AI Regulations

Generative AI is evolving faster than the laws that govern it, creating a challenging and fragmented compliance landscape for enterprise security teams. With regulations like the EU AI Act setting global precedents and various US states introducing their own rules, staying compliant is a moving target. This isn't just about avoiding fines; it's about building trust and maintaining a strong security posture. A reactive approach is no longer sufficient. Instead, you need a proactive strategy grounded in the principles of Human Risk Management (HRM).

A modern Human Risk Management (HRM) program provides the visibility and control necessary to adapt to these shifting requirements. By understanding how, where, and why employees and AI agents are interacting with sensitive systems, you can align your security controls with your legal obligations. This means translating complex legal jargon into clear, enforceable policies and targeted interventions. The goal is to build a compliance framework that is not only defensible to auditors but also resilient enough to handle the next wave of AI innovation and regulation. This requires a data-driven foundation that makes risk visible and actionable across your entire organization.

Continuously Monitor Regulatory Changes

The rules for generative AI are being written in real time. As one Deloitte report notes, these "uncertain rules" are changing so quickly that it's difficult for companies to keep up. Your legal and GRC teams cannot afford to be passive. You must actively monitor legislative developments at the local, national, and international levels to understand your obligations. This involves more than just an annual review; it requires ongoing vigilance. Designate specific team members or engage external counsel to track these changes and interpret their impact on your operations. The intelligence you gather should directly inform your risk register, acceptable use policies, and the content of your security awareness program. This proactive monitoring ensures your compliance efforts are always aligned with the current legal landscape.

Embed Compliance into Your Risk Awareness Program

Compliance can't live in a silo. To be effective, it must be woven into the fabric of your risk awareness program and your company culture. As experts at Wolters Kluwer point out, AI models trained on copyrighted material or personal information can create significant legal problems. Your policies must clearly define what constitutes acceptable AI use, including which tools are sanctioned and what data can be shared. These guidelines must then be reinforced through continuous, role-based training. For example, if a new regulation restricts using customer data in public AI models, your program should deliver targeted micro-training to the marketing and sales teams who are most likely to encounter that scenario. This turns abstract legal requirements into concrete, behavior-changing actions.

Use AI with Human Oversight to Enforce Policy

Manually monitoring for every potential compliance violation is impossible at enterprise scale. This is where you can use AI to manage AI risk. An AI-native platform can help enforce your policies by monitoring for risky activities, such as employees pasting proprietary code into an unsanctioned AI tool. As recommended, a human should always be able to check AI's work for important tasks. Following the principle of AI with human oversight, the Living Security platform can autonomously act on low-level infractions by sending an automated nudge or a policy reminder. For more serious violations, it can alert the security team for intervention, ensuring you maintain control. This approach allows you to scale enforcement efficiently, turning your policies from static documents into dynamic controls that actively reduce risk.

How to Measure Your Gen AI Risk Program's Effectiveness

Launching a Gen AI risk awareness program is a critical first step, but how do you know if it’s actually working? An effective program is one you can measure. Proving its value requires moving beyond simple completion rates and tracking tangible reductions in risk. This means establishing clear metrics, turning vast amounts of data into clear signals, and creating a feedback loop for continuous improvement. A data-driven approach not only demonstrates the program's ROI but also helps you refine your strategy to stay ahead of emerging threats. By focusing on outcomes, you can show how your efforts are directly contributing to a stronger security posture for the entire organization.

Track Key Metrics for Security Teams

To measure effectiveness, you need to know what to track. Start by establishing a baseline for key risk indicators related to Gen AI. This includes monitoring the use of unsanctioned AI tools (shadow AI), tracking data inputs into public models, and measuring the rate of policy violations. For example, you can use "model cards" to document the history and purpose of AI models, creating transparency. The goal is to see a measurable decrease in risky behaviors over time. Key metrics might include a reduction in alerts for sensitive data being used in prompts, a lower click-through rate on AI-powered phishing simulations, and an increase in employees using approved, secure AI tools. These metrics provide the concrete evidence needed to validate your risk management strategy.

Turn Behavior, Identity, and Threat Data into Actionable Intelligence

Isolated metrics only tell part of the story. The real power comes from correlating data across different sources to see the complete picture of human and AI agent risk. Human Risk Management (HRM), as defined by Living Security, achieves this by analyzing signals across employee behavior, identity and access systems, and real-time threat intelligence. Instead of just detecting an incident after the fact, this approach allows you to predict where the next one might occur. The Living Security Platform provides this actionable visibility by analyzing over 200 risk indicators. Our AI guide, Livvy, then translates this complex data into evidence-based recommendations, helping your team focus interventions on the individuals and access points that pose the greatest risk before an incident happens.

Review and Improve Your Program Continuously

A Gen AI risk program should never be a "set it and forget it" initiative. The threat landscape and the technology itself are constantly evolving, so your program must too. Use the insights you gather from your metrics and data analysis to create a continuous feedback loop. If you notice a department struggling with data leakage policies, deliver targeted micro-training. If a new AI-driven phishing technique emerges, update your simulations. It’s essential to hold regular workshops for leaders and employees to reinforce good practices and ensure human oversight is always part of the process. This iterative approach allows you to continuously improve your program’s effectiveness, adapt to new challenges, and foster a security-first culture around AI use.

Related Articles

• AI-Powered Phishing Simulations for Real Risk Reduction | Living Security
• AI Agent Risk Management: A Complete Guide
• AI Workforce Risks & Human Risk Management Strategies | Living Security
• AI for Employee Security Behavior: A Complete Guide

Frequently Asked Questions

How is managing generative AI risk different from traditional cybersecurity threats? Traditional cybersecurity often focuses on technical vulnerabilities in software or networks. Generative AI risk, however, is fundamentally a human risk. It stems from how your employees interact with these powerful tools. The primary threats, like data leakage, intellectual property exposure, and falling for AI-generated phishing, are driven by human actions. This requires a strategy that goes beyond technical controls to understand and influence behavior by correlating signals across identity, behavior, and threat data.

My employees are already using AI tools. Is blocking them the best way to reduce risk? While blocking unapproved tools might seem like a simple solution, it often drives usage underground into "shadow AI," where you have no visibility or control. A more effective strategy is to create safe guardrails. This involves establishing clear acceptable use policies, providing sanctioned and secure AI tools for employees to use, and implementing a Human Risk Management (HRM) program. This approach allows you to enable productivity and innovation safely, rather than trying to fight an unwinnable battle against user adoption.

Where should I begin when assessing my organization's specific Gen AI risks? A great starting point is to make the risk visible and measurable. You can begin by mapping out which employees are using which AI tools and for what purpose. Correlate this behavioral data with identity and access information to identify high-risk roles, such as developers with access to source code or finance teams with access to sensitive data. This data-driven assessment helps you move beyond abstract fears and pinpoint your most significant vulnerabilities, allowing you to prioritize your awareness and training efforts effectively.

How can my security team realistically manage these new risks without being overwhelmed? Managing Gen AI risk at scale requires leveraging technology to work smarter, not harder. Manually tracking every interaction is impossible. An AI-native platform, like the one from Living Security, a leader in Human Risk Management (HRM), can automate much of this process. It can analyze risk signals to predict where incidents might occur and autonomously deliver interventions like policy reminders or micro-trainings. This allows your team to focus on high-priority threats while maintaining human-in-the-loop oversight, turning policy into an active defense.

Our training program already covers phishing. Isn't that enough for AI-driven threats? Standard phishing training is a good foundation, but it's not sufficient for the sophistication of AI-driven attacks. Generative AI can create flawless, highly personalized social engineering campaigns and even deepfake audio or video that can fool a well-trained employee. Your program needs to evolve to include AI-tailored simulations that prepare employees for these advanced threats. Furthermore, a comprehensive program goes beyond phishing to address other risks like data leakage and intellectual property exposure through targeted, role-based training.