Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Whether it’s vendors diagnosing GPU driver failures or network technicians troubleshooting switch configurations, organizations are often ready to do whatever it takes to get their infrastructure back to normal. For some, that may mean defaulting to the fastest access path available for third-party access, such as shared SSH keys, VPN credentials, or screen-sharing sessions.

In 2026, the gap between AI adoption and AI oversight has become a primary boardroom concern. While generative AI has supercharged productivity, it has also introduced Shadow AI: the unmanaged, invisible use of unauthorized AI apps and autonomous agents that operate outside the view of traditional IT security. In this guide, you’ll learn why Shadow AI is exponentially harder to detect than Shadow IT and, more importantly, how to build a modern detection framework. We’ll explore.

On April 29, 2026, a Linux kernel local privilege escalation vulnerability was publicly disclosed under the name "Copy Fail" (CVE-2026-31431). Cloudflare’s Security and Engineering teams began assessing the vulnerability as soon as it was disclosed. We reviewed the exploit technique, evaluated exposure across our infrastructure, and validated that our existing behavioral detections could identify the exploit pattern within minutes.

Every security leader knows the sequence. A new threat breaks; reports start circulating, and within minutes the same questions are moving through the organization. Are we exposed? Does this change our risk? What are we doing about it?

For most organizations, answering these questions is slow, manual, and difficult to defend. Analysts must interpret threat reports, build SIEM queries, run retroactive searches, and validate findings under pressure. The result is delayed answers, inconsistent processes, and limited confidence at the executive level. This is the gap between threat awareness and proof of exposure. It is where operational risk and board-level scrutiny converge.

Modern security teams are not lacking data. They are drowning in it. Threat intelligence feeds, indicators, campaigns, internal detections, and investigation artifacts are constantly growing in volume and complexity. Yet when analysts need answers, they are often forced to manually search, pivot, correlate, and interpret across multiple data points. This creates a familiar problem: too much data, not enough clarity.

The proliferation of credentials outside centralized visibility and control is known as “credential sprawl,” and attackers are eager to take advantage of it. Unfortunately, credential management is a broad problem that only grows in complexity as organizations add new tools, employees, and partners.

Setting up and managing client environments often involves repetitive, manual work. Each new managed company requires policy setup, access configuration, and ongoing oversight. Repeating this across environments slows onboarding, introduces inconsistencies, and makes it harder to maintain control.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo 92% of security leaders say something is actively reducing their trust in AI within the SOC. These aren’t skeptics, they’re people who have already adopted AI and believe in its ability to enhance security operations. We know from the 2026 AI SOC Leadership Report that AI is already widely adopted in the SOC, with 94% of organizations using it in some capacity.

Agentic AI is forcing a hard question on every security leader: when your SOC is full of autonomous “doers” instead of just dashboards and scripts, is that your new best friend or a brand‑new risk surface you barely understand? The honest answer is both, and the way you design, govern, and deploy these systems will decide which side wins.