Agentic AI | Breaking Cybersecurity News | The Hacker News

Introduction The average enterprise security team has 40 or more security tools, giving a lot of visibility into internal telemetry and asset data. But often, these tools are working in siloes, generating (overlapping) alerts and data. And yet, breach dwell times remain stubbornly long (~43 days), response windows keep closing before teams can act, and analysts burn out triaging noise instead of stopping threats. The problem isn't effort. It's architecture. Security programs were built for a world where threats moved slowly enough for humans to coordinate responses manually. That world no longer exists. With the way AI capabilities are getting developed and used, especially with frontier AI tools, a much more proactive stance to security is needed as well as machine speed response to combat fast moving adversaries. Gartner's Continuous Threat Exposure Management (CTEM) framework helps this shift from reactive, point-in-time assessments to a continuous, iterative cycl...

The first wave of enterprise AI concern was straightforward. It was simply employees pasting sensitive data into public AI tools. Security teams responded with usage policies, domain blocks, and data loss prevention rules. That response made sense at the time. It doesn't fit the problem anymore. Shadow AI has shifted from a data leakage concern to an access control problem. The threat isn't about what employees type into AI tools. It's about which AI agents are running inside the organization, what enterprise systems they're connected to, and what actions they're authorized,or not, to take. From passive tools to active actors Employees and business units are building AI agents at a pace most security teams can't keep track of. Custom assistants, coding agents, workflow automations, and agentic applications are being created across departments with some in sanctioned platforms, but many through browser extensions, SaaS-native features, developer tools, M...

Eighteen months ago, the AI SOC was a marketing line. Today it's a budget item. The category has crossed over from interesting to inevitable, with billions of dollars now flowing into AI-powered security operations platforms, agentic SOC tools, and AI co-pilots built into every layer of the security stack. The data shows SOCs are buying, deploying, and standing up AI capabilities at the fastest pace the industry has ever seen. And yet, the same SOCs reporting record AI adoption are reporting underwhelming outcomes. The first objective benchmark on the value of AI in the SOC was published in the SOC-CMM 2026 Maturity Report in May, drawing on survey data collected from roughly 200 SOCs across regions, sectors, and delivery models between late January and mid-March 2026. Only about 10% of respondents said AI has delivered excellent value to their SOC. About 19% reported good value. The remaining 71% landed at some value or none at all. Eighteen months into AI deployment, that...

Over the past several weeks, the cybersecurity community has been reminded how quickly frontier and agentic AI in defense networks can challenge our assumptions. When Anthropic's Claude Mythos model was made available to a limited set of organizations as a technical preview, it was reported that an unauthorized group claimed that it had gained access within hours. The incident, if true, was more than a possible breach. It was a warning. The potential impact of advanced AI on U.S. defense and intelligence networks is significant. As the U.S. government moves to deploy AI capabilities on classified networks, the opportunity is clear: advanced AI can help accelerate decision superiority for American forces . But the risks are expanding just as quickly, particularly as agentic AI begins to operate across sensitive networks, data environments, and mission workflows. AI adoption is not simply about deploying powerful models. It requires the right security, governance, and resilient...

Ask a cybersecurity pro about Network Detection and Response (NDR) and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positives. The old complaint lingers in part because reputations are sticky, and because NDR has evolved faster than the narrative. The origins of noise NDR deployments have always given analysts deep visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often came as raw material, not finished intelligence. Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn't invest that time (or didn't know how important it was) helped cement NDR's "alert firehose" or "noisy" reputation. NDR with agentic AI turns noise into narrative A...

Agentic AI is already running in production environments across many organizations today. It is executing tasks, consuming data, and taking actions — most likely without meaningful involvement from the security team. The industry conversation has largely framed this as a question of policy: allow it, restrict it, or monitor it? However, that framing misses the point.  The more urgent question is whether security professionals actually understand what they are dealing with. In most organizations, they don't right now. And that gap is compounding by the week. You cannot secure what you do not understand The foundational principle of information security has not changed: genuine fluency in a technology must come before you can meaningfully defend it. Think about firewalls. You cannot configure one well without understanding networking. When cloud computing arrived, organizations that skipped the foundational work ended up with environments they could not reason about — tools purc...

Cybersecurity researchers have disclosed details of a new attack called CometJacking targeting Perplexity's agentic AI browser Comet by embedding malicious prompts within a seemingly innocuous link to siphon sensitive data, including from connected services, like email and calendar. The sneaky prompt injection attack plays out in the form of a malicious link that, when clicked, triggers the unexpected behavior unbeknownst to the victims. "CometJacking shows how a single, weaponized URL can quietly flip an AI browser from a trusted co-pilot to an insider threat," Michelle Levy, Head of Security Research at LayerX, said in a statement shared with The Hacker News. "This isn't just about stealing data; it's about hijacking the agent that already has the keys. Our research proves that trivial obfuscation can bypass data exfiltration checks and pull email, calendar, and connector data off-box in one click. AI-native browsers need security-by-design for agent...