Navigating AI Implementation: Addressing the Security Concerns

There’s nothing risky about AI on its own. As with any tool, it’s how you use it that counts. Most security issues with AI can be managed by understanding the risks associated with different use cases. In this post, we’ll summarize a few important areas to focus on when reviewing the security profile of your AI. We’ll also look at one important way Agent Assist tools can bolster your security measures.

Data Privacy and Sharing

Protecting personal data is the focus of many data privacy regulations and policies. Highly regulated industries like banking and healthcare tend to have the strictest regulations and harshest penalties for security lapses. Surely, you can imagine why - identity theft, fraud, compromised medical care etc. In recent years, customers have pushed back against mass data collection and are wary of how companies might use their sensitive data.

So, data privacy is a good place to start when evaluating your AI security profile. 

The key question is how much your AI model relies on customer data. 

If you’re using AI to profile customers and learn from their personal information, you’re going to have to be much more careful than if your model is just analyzing sentiment in a conversation.

Compliance with Regulations

AI implementations must comply with local and international regulations. This compliance is not just a legal necessity but also a crucial component of risk mitigation strategies concerning data privacy and operational procedures. Adherence to standards like ISO 27001 and SOC2 Type 2 is essential, as these certifications demonstrate a company’s commitment to security, ensure operational integrity, and help build trust with users and clients.

The Role of AI and Human Oversight

Large language models like ChatGPT can process huge amounts of data to deliver impressive answers in seconds. But they can also make mistakes. The consequences of such errors are not trivial and can be costly in monetary and reputational terms. Let’s be honest, nobody wants bad advice from AI to land them in court and in the news, like what happened when Air Canada was held responsible for their chatbot’s bad advice.

Therefore, maintaining human oversight is critical - having a human in the loop ensures that errors can be identified and corrected before they impact customers. This approach not only enhances reliability but also maintains the accuracy of AI systems. 

Organizational Readiness and Training

Organizational readiness and training are foundational pillars for successful implementation and utilization of AI tools within a company. However, a significant gap often exists in the readiness and knowledge base of companies regarding secure AI implementation.

Being ready means having a full understanding of the implications, challenges, and opportunities that AI brings to the table. This involves assessing the current state of your organization's infrastructure, processes, and workforce capabilities to determine the readiness to adopt AI technologies.

🙋‍♂️ Tip: In order to “do AI” right, you first need to perform a gap analysis. Just because AI is complex, doesn't mean you need to reinvent the wheel. Start by assessing readiness, then identify the gaps and finally invest in ongoing education. This will equip your teams with the knowledge to manage AI securely and effectively.

AI Doesn’t Get Tired

So far, we’ve looked at the potential security risks of implementing AI, but AI can be a powerful element of your security infrastructure too. The ability to recognize patterns can detect threats and flag suspicious communications. For instance, sentiment analysis tools can pick up on cues that a customer support agent may be interacting with a scammer.

Cyber crooks who target support agents will often try to exhaust an agent so they become careless. But AI doesn’t get tired. The same Agent Assist tools that pick up on subtle cues to drive a conversation and offer solutions can screen an interaction for bad intentions.

AI security is a human task…

Your key takeaway from this post:

AI can be safe or risky depending on its application and the humans using it. By understanding the strengths and vulnerabilities of AI, you can implement it more securely and train your team to provide human oversight where it’s needed. As always, these two elements work better together. AI with a human in the loop is more secure than either AI or a human would be alone.