AI in the enterprise: three ways to mitigate AI’s security and privacy risks

Artificial Intelligence (AI) has the potential to revolutionize how businesses operate. But with this exciting advancement come new challenges that cannot be ignored. For proactive security and IT leaders, prioritizing security and privacy in AI can’t simply be a box-checking exercise; it's the key to unlocking the full potential of this wave of innovation. 

The success of AI systems in enterprise environments hinges on access to extensive proprietary data, which increases the risk of cyberattacks and data breaches. To protect sensitive information from falling into the wrong hands, it is crucial to implement robust technologies and security measures, such as encryption, authentication protocols, and regular vulnerability assessments.

Equally, the threat of internal breaches shouldn’t be underestimated; it must be addressed through clear policies, stringent access controls, and vigilant monitoring.

Security and IT leaders must carefully consider these factors while also prioritizing other goals on their roadmaps. Ultimately, successful AI implementation is not always as seamless as some vendors might claim.

The underwhelming impact of AI in enterprises 

AI is being rapidly adopted by numerous technology companies. If they aren't already integrating AI, it's likely they will be soon, fundamentally transforming your product experience.

This is because AI has the potential to empower a "do more with less" strategy. At a macro level, examining the evolution of technology reveals a continuous progression through abstraction layers that enhance accessibility and boost productivity. The addition of large language models (LLMs) represents yet another layer of abstraction that reduces barriers to entry. Organizations no longer need to hire elusive "unicorn" talent to leverage these technologies effectively, and solve ongoing challenges like alert fatigue and burnout. 

Despite its immense potential, AI's impact on the enterprise has been underwhelming so far.

This can largely be attributed to rigid products that struggle to effectively connect data across technology stacks and workflows, alongside ongoing security and privacy concerns. 

AI systems are only as good as the data they can access. If the system is not secure, your data is at risk. Many teams are struggling to integrate AI into their existing workflows, leading to inefficiencies and suboptimal outcomes. Automation and orchestration play crucial roles within the larger generative AI ecosystem. The lack of skilled professionals who can properly implement and manage AI-powered workflow solutions further complicates the situation. As a result, businesses are often unable to fully leverage the potential of AI to drive innovation and achieve success.

The pressure to adopt AI 

Many organizations are still in the experimental phase of integrating LLMs internally.

When comparing LLMs to traditional machine learning, it's important to note that research is just beginning to uncover new safety issues. We are merely scratching the surface of what these technologies can achieve.

Still, security and IT leaders face enormous pressure to chart a clear path forward. They must navigate the security and privacy concerns associated with AI, adding to an already overwhelming list of responsibilities on their roadmap.

Budget considerations also add to the pressure. If company funds are allocated to AI, CISOs need to take the time to understand how their current products are utilizing AI to determine whether they should redirect that budget towards enhancing those offerings. It's crucial to ensure the Return on Investment (ROI) aligns with existing business goals. AI systems should support the company’s mission, and if the metrics show improvement, it indicates success. While this approach may seem simple, it is vital for achieving goals.

While AI policies are being implemented, there continues to be challenges with unauthorized tools and "shadow AI," where employees unknowingly input sensitive data into seemingly harmless prompts. This data is then collected and stored by large language models, creating a security blind spot that can be exploited in the event of a breach.

AI security: 3 ways to mitigate AI's security and privacy risks 

Mitigating the risks of generative AI hinges on three crucial pillars: employee awareness, robust security frameworks, and advanced technology.

1. Employee awareness 

First and foremost, security and IT leaders must prioritize employee education and training. 85% of data breaches have a human aspect. Building a culture of cybersecurity starts with educating employees on the risks of sharing sensitive data through AI prompts and providing clear guidelines for handling company information. Employees should also be trained to identify and report potential security threats both externally and internally, without fear of retribution.

Additionally, companies must implement and communicate security policies on access to AI systems, guiding how to use them and under what circumstances. Regular training and reminders about the importance of data security can instill a security-first mindset among employees, reducing the likelihood of accidental data leaks.

2. Robust security frameworks 

Another important aspect of addressing AI security risks is implementing robust security frameworks. Conduct thorough risk assessments, identify potential vulnerabilities, and implement appropriate controls and protocols to mitigate these risks. You should also include regular penetration testing and vulnerability scanning to identify any weaknesses in your systems.

Update the incident response plan already have in place, outlining steps to be taken in case of a data breach or other security incident involving AI systems.

3. Advanced technology systems 

Finally, companies must leverage advanced technology solutions to enhance their AI security strategies. Using a large language model (LLM) is not the solution for every problem, and the presence of AI in a tool does not guarantee its suitability for your needs.

If you're considering an AI tool, it's important to ask how your data will be used, what the model has been trained on, whether it is unbiased, and if it will continue to learn from your data. If you prefer that it trains on your data privately, consider the quality of that data. Additionally, be aware of any outdated policies and procedures—training on poor information will lead to poor outcomes.

With AI becoming an increasingly integral of business operations, security and privacy must be top priorities. By prioritizing security and privacy, you can protect your organization and build trust with your customers and stakeholders.