Data exfiltration in an AI world
It is undeniable at this point in time that the value of your enterprise data has risen with the growth of large language models and AI-driven analytics. This has made data even more of a target for bad actors and increased the damage resulting from malicious or accidental exposures. Sadly, this is the new reality for CISOs, with data exfiltration creating unprecedented risks. Stolen datasets can now be used to train competitor AI models. And with powerful AI techniques that extract deep details from stolen datasets, even small data losses can have seismic impacts.
Human error in data loss
Human error remains a critical weak link in data loss. For example, employees might inadvertently broadcast corporate secrets by inputting sensitive company information or source code into public-facing AI models and chatbots. Unfortunately, these human errors can lead to catastrophic data breaches that no policy or procedure can entirely prevent. Training and policy are critical, but mistakes can still occur, and no amount of training can change the behavior of a malicious insider.
Traditional Data Loss Prevention (DLP) solutions have been around for decades, but their adoption and effectiveness have been mixed. However, the new data theft risks in the AI era may finally push DLP into the spotlight. Modern DLP solutions are enhanced with AI capabilities and offer more automated, context-aware protection. They can better understand data patterns, user behaviors, and potential exfiltration scenarios. This evolution makes DLP more effective and less intrusive, potentially overcoming historical adoption barriers, although deployment complexity may still present a hurdle.
