Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A maturity score answers where a program has been. It reports the state of documented process at the moment somebody assessed it, on a cadence measured in quarters or years, using a scale that describes organization rather than outcome. Every property that makes it useful for planning makes it useless as an early warning. ‍ The interesting question is what a leading indicator would look like instead, and the answer requires separating two problems that get treated as one.

Two teams inventory the same organization and return different numbers. One counts forty-one AI assets, the other counts one hundred and twelve. Neither is wrong, because they counted different things, and nobody had decided what a row represents. ‍ Guidance on building an AI inventory covers which fields a row should carry and skips what a row is. That question determines the count, the risk scores, the regulatory classification and whether two inventories can ever be reconciled.

I recently had the opportunity to speak at the ISACA GRC Conference in San Diego about a challenge I see becoming increasingly important for governance, risk, and compliance teams: How do you prove your controls are actually working in digital environments that never stop changing?

Ask any security engineer what they actually think about their vulnerability scanner, and you will get a version of the same answer. They trust maybe 20% of what shows up in the patching queue. The rest gets a suspicious glance, and a slow death in a backlog. That is the real cost of a false positive. It is quiet, it compounds, and it hollows the tool out from the inside. It is also the reason autonomous pentesting came to replace hypotheses with confirmed exploits.

Cyber attacks cost UK businesses an estimated £14.7bn every year. The average cost of a significant breach for an individual business sits at almost £195,000. Half of all small businesses have experienced at least one attack in the past 12 months. For medium and large organisations, that figure rises to 82%. Those numbers should focus the mind.

BlueVoyant researchers identified a new deployment method for the FireClient backdoor during investigations into Microsoft Teams-based social engineering campaigns.

A critical alert enters the SOC queue during the overnight shift. By morning, the dashboard shows an acceptable headline MTTR because the incident was closed quickly after an analyst finally picked it up. The timeline tells a different story: the alert sat unassigned for nine hours because severity routing sent it to the wrong queue. The team optimized the visible number while leaving the dangerous delay untouched.

Adversaries are using AI to launch cyber attacks in record time, forcing security teams to measure responses in minutes instead of months. Phishing campaigns built with large language models (LLMs) achieve click-through rates 4.5 times higher than traditional methods,1 and the average time between initial compromise and lateral movement has fallen to just 29 minutes.2 This is a 65% increase from the prior year.2 State and local governments and higher education institutions are at an inflection point.

Consolidating every security finding into one place solves visibility, but it doesn’t reduce risk on its own. Aggregation without context just makes the backlog feel bigger. Real remediation depends on answering four questions about each exposure: what needs fixing, why it matters, where the fix happens, and who owns it.

‍Last updated: August 20, 2026‍ A supplier breach or a tough question from a regulator can force a rushed third-party risk management (TPRM) evaluation. You need an answer before the next steering meeting. This list compares the 12 best third-party risk management tools in 2026, based on the capabilities that separate them in daily use, so you can shortlist faster. Whether you're an analyst running early research or a CISO approving the budget, you're working from the same criteria.