“Whatever has once been thought can never be taken back.”
Friedrich Dürrenmatt
The Rise of AI
The launch of OpenAI’s ChatGPT, Microsoft’s strategic investments in AI, and Google’s Bard have drawn widespread attention. This is not to mention the myriad startups leveraging generative AI to bring automation to new heights. Never has a new app reached so many users in only a few days.
Generative AI has proven valuable for a variety of tasks: from text generation and information retrieval to summarizing complex information and even understanding source code. The technology is transformative, primarily when trained on massive datasets that are publicly available.
The drive towards embracing generative AI isn’t just a fad – it’s a necessity. As the technology becomes a staple in the digital landscape, those who hesitate to adopt it risk losing traction. Employee workflows are increasingly incorporating AI tools, regardless of company policy.
A Matter of Perspective
When it comes to the topic of AI security, the term is often understood differently depending on who you talk to. Broadly speaking, the perspectives can be segmented into two distinct categories:
Securing AI Use Cases: One group is primarily concerned with securing the AI technologies that are deployed within the business. The focus is on how to make specific applications of AI, such as text generation or fraud detection, as secure as possible. This includes considerations like data privacy, model integrity, and secure access controls.
Using AI for Security: The other group is interested in utilizing AI technologies to improve existing security measures. For example, they might deploy AI to better monitor the infrastructure, detect anomalies, or even predict potential security threats.
Adding to these complexities are societal implications. As AI becomes ubiquitous, ethical considerations such as fairness, discrimination, and inclusivity enter the arena. How can AI be designed and used in a way that is ethically sound and universally equitable?
(Do Not) Reinvent the Wheel
When it comes to AI security, there’s no need to start from scratch. Existing Information Security Management Systems (ISMS) often already have the foundational elements needed for governing AI security. Risk assessment frameworks should be modified to include AI-specific aspects such as output validation, access control, model ownership, and training data.
As you govern the applications utilized within your organization, the same vigilance should extend to your company’s use of AI technologies. Consider integrating AI security into employee training programs and offering reminders or warnings as users navigate AI-based websites. Make sure employees are aware that the usage of external AI tools could leak sensitive information.
One hurdle to consider is that of privacy. Regulations like GDPR require organizations to correct or delete personal data upon request. With current AI models, such compliance can be complex, necessitating mitigation strategies that could involve scrubbing personal data from training datasets entirely.
Capability Development
To make your ISMS AI-ready, it’s vital to train your teams to recognize the unique risks that AI brings, while not stifling innovation with excessive security measures. A restrictive approach could push employees to employ AI tools on unsecured, personal devices, creating shadow AI that is even more challenging to manage.
Establish internal consultation and support capabilities that guide teams on securely implementing AI. These should consider principles like least privilege and data minimalism. Build robust contractual agreements with vendors, clearly stipulating data access and transfer protocols that are exclusively relevant to your operations. Finally, develop output controls based on meticulously crafted rules that serve as a buffer between the AI and end-users, allowing you to validate and limit certain types of data dissemination.
Conclusion
Yes, your users are using AI tools.
Yes, you can’t stop them.
And yes, there is risk in new technology.
Transparency in security management and user-level awareness can be half of the deal in mitigating risks. While the risks associated with new technology are real, this doesn’t necessitate a complete overhaul of existing cybersecurity frameworks.
The key is to be proactive without being restrictive, to educate without intimidating, and to secure without stifling. Yes, AI is here, and yes, it comes with its own set of risks. But with thoughtful planning and strategic adaptation, there’s no need to hit the panic button—instead, it’s time to breathe.