Businesses are increasingly recognizing the value of generative AI, prompting the need for well-defined security policies tailored to its use. The rapid adoption of generative AI has raised concerns in the cybersecurity community as the technology’s introduction outpaces the establishment of guidelines, exposing organizations to security threats and data privacy risks.
For Chief Information Security Officers (CISOs), the imperative is clear: the absence of a robust AI security policy, specifically addressing generative AI, poses a significant risk. Unlike traditional AI, generative AI is evolving rapidly, offering substantial promise alongside serious security implications. Developing effective cybersecurity policies becomes a challenge for CISOs, requiring a delicate balance between supporting innovation and managing risks.
The urgency to create security policies is underscored by the swift growth in AI adoption, particularly in the use of generative AI. A Splunk/Foundry survey reveals that a significant percentage of public and private sector organizations have integrated generative AI into production systems, primarily for automation and productivity enhancement which clearly indicates the importance of ai and business. However, the potential security risks associated with third-party large language models (LLMs) and the evolving landscape of generative AI necessitate proactive policy creation.
Drawing lessons from dealing with shadow IT in the past, organizations are urged to establish security policies early in the development of generative AI technology. The challenge lies in aligning these policies with business objectives, ensuring they are not isolated within the security domain but are integral to various business functions. Business alignment becomes a critical challenge and opportunity for CISOs.
To formulate effective generative ai in company security policies, CISOs must gain a comprehensive understanding of business-specific use cases and potential risks. Data control emerges as a crucial aspect, requiring policies around data encryption, anonymization, and other security measures to prevent unauthorized access or transfer. Additionally, organizations need to assess and manage generative AI-produced content for accuracy, considering the risk of “hallucinations” and unauthorized code execution.
Generative AI-enhanced attacks, such as those leveraging realistic audio recordings or sophisticated impersonation techniques, should be addressed within security policies. Communication and training are pivotal for policy success, necessitating clear articulation of risks and responsible use of generative AI. Supply chain management and third-party considerations also play a crucial role, requiring continuous due diligence on third-party generative AI usage and risk assessment.
In conclusion, the creation of exciting and interactive generative AI security policies can contribute to better adoption and adherence by employees. By showcasing the benefits and responsible use of generative AI, organizations can position themselves as security facilitators, enabling innovation while maintaining a secure business environment.
