Businesses today are compelled to adopt a slew of AI-powered technologies, or risk falling behind the competition. Rapid integration comes at a cost, as it leaves data, systems, and your reputation more vulnerable to attacks.
This article addresses the risks your business will face if you approach AI integration haphazardly. More importantly, it outlines five key practices that minimize such risks.
The Effects of AI Integration on Business Security
Access to large amounts of data to base responses and predictions on is the prerequisite for most AI model operations. Such data may be highly sensitive, like personally identifiable user information or details related to a company’s intellectual property. If restrictions and oversight are lacking, this can result in sensitive data becoming more susceptible to leaks and breaches.
Only the largest and technologically most advanced companies have the capacity and expertise to develop all the AI tools they need in-house. The vast majority rely on services provided by third-party vendors. This raises several concerns, from loss of a company’s cybersecurity agency through third parties’ questionable data handling policies to long-term technical debt and unfavorable vendor lock-ins.
For all their transformative potential, it’s imperative to keep in mind that even the best AI models are far from infallible. Accordingly, the content they generate may also inadvertently become a risk factor. For example, AI-generated social posts or pitch decks could leak sensitive data. Similarly, overreliance on AI generation when creating instructions or code could result in outputs that include otherwise effective security measures like encryption and authentication.
Which Measures Eliminate or Mitigate These Risks?
Combining exhaustive policies with appropriate tools and practices is the most effective means of addressing AI-related risks. These are the key components of such a strategy.
1. Comprehensive internal AI regulations
A set of rules and guidelines on the general use of analytic and generative AI tools is fundamental to any risk mitigation. Internal AI regulations outline which tools employees may use and in what manner. This limits abuse and the potential problems arising from shadow AI.
A data governance policy is integral. It defines what data employees are permitted to supply AI tools with, as well as proper handling procedures. Enforcing data governance results in proper anonymization, handling, and storage, minimizing the effects of exposure and leakage.
2. LLM observability
Regardless of industry, the average employee’s most extensive and immediate exposure to AI comes through interactions with large language models. AI regulations lay the groundwork for correct usage; LLM observability enforces it.
LLM observability is the practice of tracking and logging user inputs and output responses. It’s invaluable for dealing with diverse threats. On the one hand, it helps assess model accuracy and reduce the number of hallucinations or otherwise harmful responses. On the other, LLM observability ensures appropriate user inputs, reducing both running costs and the likelihood of malicious insider attacks.
3. Strict access controls
Long-established cybersecurity best practices also significantly impact safe AI usage. Combining Zero Trust with Rule-Based Access Controls establishes a clear access hierarchy. It limits users to only those AI tools, or specific features within them, needed to perform their duties while making unauthorized access harder and less likely.
4. LLM routing
LLM routing is a multi-faceted practice. On the surface, it’s a cost-saving measure that routes simpler, high-volume requests to smaller or less expensive LLMs capable of producing satisfactory outputs.
However, LLM routing also has several security and compliance benefits. For example, it can ensure that sensitive data only interacts with internal LLMs or APIs from vetted third parties. Similarly, it can be set up so that only external providers who respect legislation like the GDPR handle data that falls under its protection.
If a model is identified as faulty or compromised, best LLM routers can also isolate and bypass it in favor of safer alternatives.
5. Third-party vetting
When choosing AI providers to partner with, excellence regarding security and integrity is as important as capability, if not more so.
Competent vendors should employ effective data handling and storage policies as well as demonstrate compliance with all relevant laws and industry standards. They should be transparent both in regard to the security measures they implement and the outputs their models generate.
Conclusion
Increased risk is an expected cost of doing business when implementing new technologies. This is especially salient when dealing with malleable and rapidly evolving ones that fall under the AI umbrella. While some risk remains inevitable, enacting the measures discussed above will make it far more manageable.