Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI adoption has accelerated across sectors today as the technology becomes easier to access and deploy. Most organizations embed it in at least one aspect of their daily operations, but doing so has also introduced new risks, such as model bias and outcome drift. ‍ There’s a growing gap between AI use and responsible oversight, and keeping up demonstrable AI governance practices is a challenge.

Generative AI systems are designed to work with real data that expects structure, rely on patterns, and infer meaning from formats, relationships, and consistency across inputs. While real data facilitates better outputs and advanced training, making these systems useful has a tradeoff – it carries privacy, security, and compliance risk. This puts business on a difficult conundrum – either you block sensitive data entirely and lose context, or accept the privacy risks of using real data.

Everyone’s chasing a smarter agent. But the model was never what held the SOC back. The sharpest LLM still won’t know your environment, your team’s past calls, or where they draw the line on risk. That lives in the layer beneath the agents: context, memory, and learning. Torq’s AI research team breaks down how we build it.

For those of you who are like me, when I first heard about the new EU AI Act, I had flashbacks to the implementation of the General Data Protection Act (GDPR) back in 2018. There are certainly a lot of similarities with the EU leading the way in consumer protections that will likely lead to more, similar legislation across the globe. I’m also reminded of the iPhone when it was introduced in the consumer market and bled into the workplace (I for one held onto my Blackberry for as long as I could).