Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

It’s Friday afternoon, and you need a quick team update. Five minutes, tops, right? You ping Slack. A few people reply, a few don’t. So, you schedule a “quick sync” to get everyone on the same page. Two hours later, you’ve spent your afternoon chasing updates instead of doing actual work. And you’ll do it all over again next week. Now picture this. You’re collecting product demo videos for an agency.

Your presence here, reading this, insinuates that something is nagging at you. Maybe it’s the Ivanti headline you saw last week or the fact that half your engineering team works from cafés, co-working spaces, and home offices you’ve never set foot in. Maybe it’s the audit coming up and that one checklist item about remote access controls you’ve been putting off. No, you’re not being paranoid. We have numbers that justify your burgeoning anxiety.

It’s 2 a.m. and the SOC has a Tier 3 page. A customer-service agent on the production cluster has just wired refund payments to seven addresses outside the approved disbursement list. The runbook is unambiguous: isolate the pod, image the disk, image the memory, root-cause within 48 hours.

Every AI workload security PoC reaches the same conversation. Platform engineering pushes back: the AI team won’t accept extra latency on inference. The security engineer hunts for benchmarks and finds a contradiction. Langfuse publishes 15% overhead. AgentOps publishes 12%. The security vendor quotes 1–2.5%. None is lying. They measure different layers.

Most “hardening” advice for AI agents is a checklist of things to configure before the agent runs. CIS Kubernetes Benchmark gates. Pod Security Standards baselines. NetworkPolicy templates. None of it’s wrong — it’s just one of four phases, the one your stack already covers. The other three are Observe, Enforce, and Reconcile. They’re where AI agents actually get breached, and they’re where most stacks have nothing.

Attack Surface Management (ASM) is the continuous process of identifying, analyzing, and reducing security exposures across an organization’s environment. It provides visibility into assets, highlights security gaps, and helps prioritize remediation based on real risk.

Artificial intelligence is rapidly becoming embedded across manufacturing environments, from engineering and design to supply chain optimisation and operations. What was once experimental is now being applied in day-to-day workflows, often driven by the need for speed, efficiency, and competitive advantage. Recent research shows that 73% of manufacturing organisations report rapid AI adoption, with 90% ranking AI as a top security priority for 2026. The direction of travel is clear.

Data discovery has fundamentally changed over the last two years. The question is no longer just "Where is our sensitive data?" Organizations that stop there have a map but no enforcement. The tools that actually reduce risk answer a harder set of questions: Where did the data come from? Where is it going? Who touched it? And can we stop it before it causes damage?

Security teams often focus governance efforts on the most popular AI tools. But the real risk question isn't which tools employees use most. It's which tools are growing fastest and what data those tools can reach. New data from Cyberhaven Labs shows that the AI categories posting the largest year-over-year growth numbers are the same categories with privileged access to source code, credentials, customer contracts, and internal architecture.

I’ve spent a lot of time recently thinking about what "innovation" actually means in an industry that moves as fast as cybersecurity. It’s a term that gets thrown around a lot, but as a product leader at Bitsight, I see it as something much deeper than just shipping new features. It's about a fundamental shift in how we help organizations stay resilient.