Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The entire world of technology is abuzz about AI/ML. It’s arguably the most disruptive technology to society since the smartphone. In fact, Gartner estimates that the number of companies using open-source AI directly will increase tenfold by 2027.

Every IT team is under pressure to “do more with AI.” A new tool promises smarter workflows, a new agent claims to replace manual tasks. But if you’re managing service requests, availability SLAs, patch cycles, infrastructure capacity, and application performance every day, you know the truth: AI doesn’t automatically reduce complexity on its own.

If shadow IT was the SaaS era’s guilty pleasure, shadow AIT is GenAI’s sugar rush: unsanctioned AI models, tools, and autonomous agents quietly wired into business workflows—often without approvals, logging, or controls. It’s fast, it’s useful, and it can bite.

The AI revolution is here, and it’s running on Kubernetes. From fraud detection systems to generative AI platforms, AI-powered applications are no longer experimental projects; they’re mission-critical infrastructure. But with great power comes great responsibility, and for Kubernetes platform teams, that means rethinking security.

The use of “shadow AI” is an increasing security risk within organizations, according to a new report from Netskope. Shadow AI is a newer variant of shadow IT, in which employees use unauthorized technology without the knowledge of the IT department. This is generally driven by a desire for increased productivity rather than malicious motives, but employees are often unaware of the risks introduced by unauthorized tools.

Risk doesn’t live in just one place—it comes from vendors, suppliers, partners, and from inside your business through processes, people, and systems. ‍ Managing that risk is often fragmented, too. Vendor reviews live in one system, internal issues in another, and leadership reports take hours to compile. And every new vendor, tool, or requirement contributes to another layer of risk.

Bitsight’s mission to keep evolving the capability of our data engine through AI enhancements hit a new milestone today. The latest addition is a new entity mapping capability added to Bitsight AI and the data engine, which uses GenAI agents to create more complete and consistent sets of identifiers for organizations scanned and added to Bitsight’s entity inventory.

Your security teams are facing an unprecedented challenge. AI agents are spreading across enterprises faster than anyone anticipated, from Microsoft 365 Copilot processing sensitive emails to custom agents built on AWS Bedrock accessing critical databases. Over 80% of Fortune 500 companies are already deploying these autonomous systems, oftentimes without adequate security guardrails. The result is a rapidly expanding attack surface that conventional security tools simply cannot see or secure.