In a recent legal showdown in Canada, Air Canada found itself in hot water when a court held the airline accountable for errors made by its chatbot while interacting with a customer. This case serves as a stark reminder of the potential pitfalls of relying too heavily on Generative Artificial Intelligence (Gen AI) without prioritizing data security.

Gen AI holds immense promise, but it comes with inherent risks, particularly when it comes to data integrity and security. The Air Canada debacle sheds light on several underlying issues that plague current Gen AI implementations.

The Underlying Issues

One glaring issue is the lack of oversight. Initially, Air Canada attempted to distance itself from the chatbot’s blunders by arguing that it operated as a separate entity. However, the court rightfully rejected this defense, emphasizing that the chatbot was an integral part of the company’s website. This highlights the importance of thorough vetting and testing of AI systems to ensure the information they provide is accurate and reliable.

Moreover, incidents like the one involving Air Canada are not isolated. Other companies have faced similar embarrassments when their Gen AI chatbots malfunctioned. For instance, DPD’s bot once went rogue, hurling profanities and tarnishing the company’s reputation. These cases underscore the need for human oversight and robust safeguards to prevent AI from causing public relations nightmares.

Another challenge is the “Black Box” problem inherent in Gen AI systems. These systems often operate in a complex manner, making it difficult to discern how they arrive at their outputs. This opacity poses a significant hurdle in identifying and rectifying biases or errors embedded within the data and algorithms.

Combating the Data Demons: How to Keep Your Gen AI Safe

To mitigate these risks and ensure the security of Gen AI systems, organizations must take proactive measures:

Data Quality is King: Prioritize high-quality, meticulously curated data for training purposes. Implement rigorous data cleaning and validation processes to weed out biases and anomalies. This entails comprehensive data discovery, classification, and labeling.

Human Oversight is Crucial: Incorporate human judgment into the development and deployment of Gen AI systems. Human oversight serves as a vital check to catch potential issues before they escalate into crises.

Security by Design: Infuse security considerations throughout the entire lifecycle of Gen AI, from data collection to model deployment. Regular security audits and penetration testing can help identify and address vulnerabilities proactively.

Transparency is Key: Foster transparency regarding the usage of Gen AI systems and the data underpinning them. This fosters trust with users and enables early detection of potential biases or misuse.

Conclusion

In conclusion, while Gen AI holds tremendous promise, safeguarding the security and integrity of training data is paramount. Smarttech247’s Managed Data Detect & Respond (MDDR) service offers a robust solution to this critical challenge. Powered by Forcepoint and Getvisibility, our comprehensive solutions empower businesses to sanitize data, discover, classify, profile, and protect sensitive information in real-time. By partnering with Smarttech247, organizations can confidently harness the power of Gen AI while mitigating security risks. Contact us today to learn more about how our MDDR service can unlock the true potential of your data.

Author: Raluca Saceanu CEO Smarttech247