Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Top tips is a weekly column where we highlight what’s trending in the tech world and share ways to stay ahead. This week, we’re taking a moment to talk about what goes on between you, your work laptop and the IT team once you log off for the day. You shut your laptop at the end of the workday. As far as you're concerned, work is over. But for your IT team, their work might just be getting started.

Most of the exposure research we publish goes one way. We count some category of internet-exposed devices, the number is bigger than you hoped, and it is climbing. Webcams. Industrial control systems. NTP servers old enough to vote. The story is almost always "there is more of this exposed than there should be, and it is getting worse.".

Most guides tell security teams to pick an access control model and move on. That advice breaks down in real environments, because the hard part isn't naming the model, it's keeping least privilege, auditability, and enforcement aligned as identities, attributes, and relationships keep changing across SaaS, cloud, endpoints, and network gear. In practice, the winners are the teams that treat access control as an operating discipline, not a diagram in an architecture deck.

Every SaaS tool your organization onboards now carries a hidden layer of AI risk. The chatbot on your CRM, the transcription service your sales team runs, the code assistant embedded in your IDE. Each one processes company data through models you did not build, in ways your vendor questionnaire was not written to catch. Traditional third-party risk management was designed to evaluate infrastructure, access controls, and data handling.

Monitoring AI agents in production is a fundamentally different problem from monitoring traditional software or even generative AI models. Because agents run autonomously, chain multi-step reasoning across tools and systems, and change behavior as their underlying models evolve, standard software metrics like uptime and CPU utilization miss almost everything that matters. ‍

Enterprise AI guardrails are the technical controls that prevent AI systems from doing things they shouldn't, applied at the moment of execution rather than after the fact. They sit between the AI model or agent and the systems, data, and users it interacts with, filtering inputs, inspecting outputs, and constraining behavior against enterprise policy.

Ask most security tools what an AI coding agent just built, and they can tell you. Ask what it was allowed to consume before it started, and far fewer have an answer. That gap, between watching agentic development and controlling it, is what securing it actually comes down to. Securing agentic development means stopping risk before it enters a build, not flagging it after. And risk prevention has a prerequisite most approaches skip: you can only account for the assets you actually hold and manage.

It's 6:30am and you hear the door of the nightclub you've spent the last 8 hours inside shriek as it closes behind you. You watch bleary-eyed as an overly bright sunrise illuminates the business-suited people as they glide effortlessly along the sidewalk, their obnoxiously well-rested faces talking about work on their fully charged phones. You wonder, "Where did all the fun people go? And what happened to my wallet?".