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.

For years, AI and machine learning workloads lived in the lab. They ran as internal experiments, batch jobs in isolated clusters, or offline data pipelines. Security focused on internal access controls and protecting the data perimeter. That model no longer holds. Today, AI models are increasingly part of production traffic, which is driving new challenges around securing AI workloads in Kubernetes.

Phishing attacks are everywhere these days. People encounter them while shopping, job hunting, reading work emails, and checking personal texts. Thanks to AI-powered scammers, phishing has become both more common and harder to spot, leading to disastrous consequences. A phishing attack on a business costs an average of $4.8 million, and attacks on individuals can drain bank accounts and wreck credit scores.