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As generative AI (GenAI) continues to advance, integrating these innovative technologies into the workplace presents both opportunities and risks. While GenAI can revolutionize workflows and drive efficiency, it also introduces new vulnerabilities that must be managed. Ensuring employees are educated about both the positive applications and the potential dangers of GenAI is crucial for maintaining a secure work environment.

In recent years, disruptive technologies such as web apps, mobile apps, and SaaS have significantly transformed the way we work. GenAI is following this trend, reshaping industries and creating new opportunities. Initially, concerns centered around information leakage into large, publicly hosted large language models (LLMs). Today, the focus has shifted to the risk of “hallucinations”—inaccurate data generated by these models—undermining business processes that rely on accurate information.

For enterprises, the key to safely leveraging GenAI lies in identifying valuable use cases and implementing appropriate controls. Establishing a GenAI governance model requires a collaborative approach, bringing together expertise from different disciplines into a cross-functional team that should include representatives from Data Science, Legal, IT Architecture, Cybersecurity, Customer Communications, Human Resources, and Finance. Ideally, this team should be led by the Chief Information Security Officer (CISO), who can guide the creation of governance principles, policies, and practices specific to GenAI. The team’s role includes:

  • Developing high-level principles to guide the use of LLMs.
  • Developing an educational approach.
  • Creating specific policies for GenAI governance.
  • Implementing an intake process for evaluating potential use cases.

Principles

Principles provide overarching guidance for determining which GenAI use cases are worth pursuing. Examples include:

  • Avoiding the use of LLMs to process sensitive customer information.
  • Leveraging LLMs to enhance employee experiences by automating repetitive tasks.
  • Ensuring accountability for the resilience of systems using LLMs lies with system designers and implementers.

Policies and Processes

Policies should be tailored to address the unique organization-specific challenges posed by GenAI, such as establishing requirements for project teams to submit detailed use case descriptions and ensuring traceability/ownership for all LLM models used within the enterprise. 

  • Traceability is vital for understanding how LLMs are accessed and used. Integrating log files into a Security Event and Incident Management (SEIM) platform or data lake can provide the necessary observability. This ensures compliance with evolving regulatory requirements and helps enforce access accountability.
  • When incorporating GenAI into software development, enterprises should enhance their software development pipelines to include tools that identify LLM components and ensure their security. This includes maintaining a software bill of materials (SBOM) to manage risks associated with open-source components and proprietary software.
  • Most enterprises have third-party governance controls, but these may be insufficient for GenAI. Understanding how third-party products integrate LLMs is critical for maintaining security.

Program

Topics to include when establishing an employee education program on the risks and positive uses of GenAI in the workplace are:

  1. Understanding Generative AI and Inherent Risks
  • Objective: Ensure employees have a foundational understanding of what GenAI is and how it works.
  • Objective: Educate employees on the potential risks associated with GenAI to create a security-conscious mindset:
    • Data Privacy and Security: Discuss the risks of information leakage and the importance of handling sensitive data appropriately.
    • Hallucinations and Inaccuracies: Highlight the issue of hallucinations in AI-generated content and its impact on business processes.
    • Intellectual Property Concerns: Explain the challenges related to intellectual property, including protecting content and ensuring proper use of copyrighted materials.
  1. Promoting Positive Uses
  • Objective: Encourage the safe and effective use of GenAI to improve workflows and drive innovation.
    • Efficiency and Automation: Show how GenAI can automate repetitive tasks, enhance productivity, and improve the quality of work life.
    • Innovation and Creativity: Provide examples of how GenAI can be used to foster creativity and generate new ideas within the enterprise.
  1. Establishing Governance and Controls
  • Objective: Ensure employees understand the governance structures and controls in place to manage GenAI safely.
    • Governance Principles: Educate employees on the enterprise’s principles regarding the use of LLMs, such as avoiding processing sensitive information and ensuring accountability.
    • Policies and Procedures: Detail the policies that govern the use of GenAI, including the need for use case descriptions and the requirement for traceability.
  1. Implementing Best Practices
  • Objective: Teach employees best practices for integrating and using GenAI in their daily work.
    • Secure Development Practices: Instruct on secure software development practices, including the use of software bill of materials (SBOM) and tools for identifying security vulnerabilities. Vendor and Third-Party Management: Explain the importance of vetting third-party vendors and ensuring they comply with GenAI governance requirements.
  1. Monitoring and Continuous Improvement
  • Objective: Emphasize the need for ongoing monitoring and improvement of GenAI applications and processes.
    • Observability and Traceability: Train employees on the importance of maintaining logs and monitoring access to LLMs to ensure compliance and accountability. 
    • Feedback and Lessons Learned: Encourage employees to provide feedback on GenAI use cases and share lessons learned to improve future implementations.
  1. Ethical and Responsible AI Use
  • Objective: Nurture an ethical approach to AI use within the organization. 
    • Bias and Fairness: Educate on the potential for bias in AI models and the importance of fairness and inclusivity in AI applications.
    • Regulatory Compliance: Inform employees about relevant regulations such as GDPR, CCPA, and the EU AI Act, and how the enterprise ensures compliance.

Conclusion

Educating employees about the risks and benefits of GenAI is key for safe and effective implementation. By developing clear governance principles, creating robust policies, and ensuring traceability, enterprises can harness the truly awesome power of GenAI while mitigating associated risks. A proactive approach to GenAI governance will enable organizations to innovate securely and sustainably.

 

For more detailed information about the critical need for robust governance, including employee education, to manage GenAI risks, click here to download Governing Creativity Securely: Navigating the Intersection of GenAI and Cyber.

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