Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If your vulnerability management program runs on a fixed scan-and-patch cadence, whether monthly, quarterly, or tied to a compliance deadline, you are measuring your response time against an attacker timeline that continues to accelerate. Vulnerability management remains a foundational security discipline. It identifies real, exploitable flaws and gives teams a structured way to prioritize and remediate them.

A public body running a high-risk AI system owes a fundamental rights impact assessment under Article 27 before first use, with the results notified to a market surveillance authority. The obligation is real and it is not yet in force. ‍ Regulation (EU) 2026/1744, in force since July 2026, deferred the section of the Act containing Article 27 to December 2027 for standalone high-risk systems and August 2028 for those embedded in regulated products.

For over a decade, the security industry has focused on one thing: training people not to click. Phishing simulations, awareness campaigns, behavioral nudges. The human is the attack surface. Harden the human, harden the org. And that's still true. But it's no longer the whole picture.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Healthcare, as an industry vertical, is moving faster on agentic AI than it has in past technology evolutions. Some reports say it is outpacing other regulated industries. Ambient scribes are documenting patient visits in real time. Prior-authorization and revenue-cycle agents are handling payer workflows that used to require staff to log into multiple systems manually.

Researchers at Microsoft are tracking an AI-assisted phishing campaign that sent over a million emails attempting to conduct payment diversion scams.

A recruitment system faces two obligations at once. AI rules classify it as high-risk and require the governance that follows. Employment law has required attention to discriminatory effect for decades, independent of any technology. ‍ They are not two versions of the same requirement.

Saying a control reduces exposure is easy and almost always true. Saying it reduces exposure by a specific amount is a different claim, and the machinery for producing one is well established. Set a baseline from frequency and magnitude ranges, simulate, re-estimate the ranges with the control in place, simulate again, and report the difference. ‍ The method is sound. Applied to AI controls it runs into two problems, one about which term the control touches and one about what the estimate rests on.

Evo already knows which AI Assets your teams pulled into your repos, which MCP servers and skills are sitting on your developer machines, which of them carry risk, and which policies they break. Getting to any of it created friction: you leave the tool you are working in, filter a UI, export a CSV, and rebuild the chart you built last quarter, every time it’s needed.