Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

“Is ChatGPT safe” is the headline question that nearly every team asks the moment AI enters the room. The better version is: safe for what, and under which controls? Safety is not a single switch. It combines technical security, data privacy, content safeguards, governance, and how your people use the tool. This guide breaks down how ChatGPT handles data, where privacy risks actually come from, and the practical steps to operate safely at home and at work.

Low-code workflow builders have flourished in the AI wave, providing the “shovels and picks” for non-technical users to make AI-powered apps. Flowise is one of those tools and, like others in its category, it has the potential to leak data when configured without user authentication. To understand the risk of misconfigured Flowise instances, we investigated over a hundred data exposures found in the wild.

Having joined visionary leaders and top practitioners at ZenityLabs’ AI Agent Security Summit in San Francisco, I came away inspired and laser-focused on the incredible opportunities and responsibilities ahead for any organization looking to adopt and secure AI agents.

AI-powered SOCs are dominating industry conversations, yet security leaders remain split on whether a truly autonomous SOC can ever exist. Despite certain vendors aggressively marketing fully autonomous SOC solutions, Gartner's analysis "Predict 2025: There Will Never Be an Autonomous SOC" suggests solutions in the market are unlikely to deliver against claims of full autonomy. As someone who has run SOCs, I agree. Full autonomy isn’t the answer.

Artificial intelligence is no longer just an emerging technology in security operations and modern SIEMs. AI agents now enrich alerts, drive investigations, generate reports, and increasingly act as extensions of human analysts. But as with any powerful technology, new risk surfaces follow. From the Exabeam perspective, every entity in your environment deserves protection.

Prompt guardrails are a common first line of defense against client-level LLM application attacks, such as prompt injection and context poisoning. They’re also a critical component of a full defense-in-depth strategy for LLM security at the infrastructure, supply chain, and application level. The specific guardrails that teams implement depend highly on use case, but they are typically designed to.