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Penetration testing is intended to help organizations identify weaknesses before attackers can exploit them. Once testing ends, findings must be documented, reviewed, formatted, delivered, assigned, tracked, remediated, and eventually retested. In many organizations, each of those steps happens in a different system and depends on a manual handoff. Testers work in one set of tools. Reports are assembled in Word or spreadsheets. Findings are delivered through PDFs. Security teams recreate them in ticketing systems. Engineering teams update remediation status somewhere else. Retesting is coordinated through email or meetings. By the time the right owner receives the information needed to act, days or weeks may have passed. At PlexTrac , we see this as one of the largest operational gaps in modern offensive security: organizations have invested in finding vulnerabilities, but the process surrounding the pentest has not kept pace. The next phase of pentest modernization is removin...

Identity is the attack surface now. Most identity governance and administration (IGA) programs still run on manual certifications, static role models, and quarterly reviews that go stale the day someone signs off on them. That's not a compliance inconvenience for a CISO. It's a structural gap. Attackers don't wait for the next recertification cycle, so identity risk detection can't either. Autonomous identity governance turns IGA from a periodic, human-driven exercise into something that runs continuously, watching real usage, learning what normal looks like, and acting on deviations before they turn into incidents. That autonomy applies across every identity and entitlement placed under governance, continuously reassessing access as usage, roles, and risk signals change. Three things are colliding to force this shift. Identity sprawl across cloud and SaaS environments has grown past what manual reviews can realistically handle, service accounts and non-human identi...

An AI SOC agent promises faster triage, fewer missed alerts, and analysts freed from repetitive work. This guide examines what an AI SOC Agent actually delivers, the prerequisites for AI-driven SOC automation, how to test explainability, and the practical benchmarks security leaders should use before signing a contract. Why your SOC needs an AI SOC agent today Most security operations centers are not failing because they lack detection technology. They are failing because the volume of signals produced by that technology exceeds what a human team can review with any consistency. A mid-sized organization routinely generates tens of thousands of alerts per week across endpoint, identity, cloud, and network telemetry, and analyst attention is the scarcest resource in the building. The pressures forcing the conversation Alert volume outpacing headcount: Detection coverage keeps expanding while SOC staffing stays flat or shrinks. Attacker speed: Credential abuse and ransomw...

A security leader at a global finance company told us recently that his team discovered three times more AI tools running in their environment than IT had approved. Nobody had smuggled them in. Employees had simply pointed agents at their work, and the agents brought their own tools with them. That conversation is not unusual. It is the conversation. Over the past year, in customer discussions across finance, healthcare, manufacturing, and government, the same four struggles come up so consistently that we have started treating them as the shape of the problem itself. Every company effectively hired a second workforce this year, human workers and agentic workers side by side, and the agentic workers never went through onboarding. No handbook, no scoped credentials, no acceptable-use policy they can actually read. Here is what teams are struggling with, what our research says about why, and what closing each gap actually requires.

When it comes to email security software, the cybersecurity industry has become very good at one thing: scoring the message that's already in the inbox. We've gone from blocklists to signature matching to behavioral ML, and each generation of email security innovation was a genuine improvement over the last.  But the losses keep climbing anyway. The median time it takes someone to click a phishing link is just 21 seconds after opening the email, and another 28 seconds to hand over credentials or payment data. That's under a minute start to finish. That number hasn't moved much in years despite everything we've thrown at the problem. It's time for a different approach. But first, we need to understand modern challenges. We're filtering messages, but attackers are running campaigns

Somewhere right now, malware running on a compromised machine is checking in with an AI model, asking it for a new version of itself. Google's Threat Intelligence Group caught this happening in late 2025. The malware, nicknamed PROMPTFLUX, does this every hour it runs, and each version comes back looking different from the last. By the time a security tool learns to recognize it, it has already changed shape again. This isn't a rare glitch or a lab experiment. It's a preview of how a growing share of attacks work today and why legacy SIEM platforms built to detect known patterns are starting to fall behind. Legacy security tools were built to detect, not to adapt A SIEM is a system that collects logs from every part of a company's network and looks for signs of an attack. For years, it worked like a security guard with a very long memory. The guard learns what a break-in looks like - a certain kind of file, a pattern of behavior, a code signature, and watches fo...

We had an enterprise customer tell us their entire AI footprint was Copilot. That was the whole answer. One tool, one line item, done. We ran the first scan. Copilot wasn't even close to number one. Claude was. OpenAI came in second. Copilot was third. Nobody on the security team knew because Claude doesn't show up the way a SaaS app used to show up. There's no single login screen, no single admin console, no one place to look. That's the part most security teams miss. Claude isn't one surface. It's six. The six places Claude actually runs Claude Enterprise and Connected Apps. This is the surface everyone pictures: employees typing into Claude, OAuth'd into Google Drive, GitHub, Slack, and Jira, asking Claude to act on what's inside. The audit log shows that a connection happened. It does not show what got pulled into the prompt or what came back out. A finance analyst can drop a quarter of board materials into a conversation in ten second...

Security is fundamentally about knowing what a system is supposed to do, then catching it when it does something else. For software with deterministic execution paths, that is a tractable problem. For AI agents, it is not. An agent does not follow a fixed code path. It reasons toward a goal, selects tools based on that reasoning, and adapts its next action based on what those tools return. The same input can produce a different sequence of actions depending on context, session history, and what an external tool happened to return. Behavior is the attack surface, and behavior changes. Traditional proxies and AI firewalls were built to inspect content: what a user sent, what a model returned. Intent security asks a different question: is this agent doing what it was built to do, in this context, for this user, right now? Answering that requires building a behavioral baseline for every agent and measuring deviation from it continuously. That is the problem Lasso was built to solve....

For a long time, the cybersecurity industry has framed social engineering as a psychological problem. We treat it as a battle of wits between a charismatic con artist and an unsuspecting employee. The prevailing wisdom says that if we just train our people to better identify scams or tear down malicious infrastructure slightly faster, we can stay ahead of scammers. But looking at the threat landscape from a purely threat-intelligence perspective tells us something entirely different: Modern social engineering is more than a psychological game. It's a highly optimized, industrialized deception economy. Attackers run campaigns like hyper-efficient businesses. They have Customer Acquisition Costs, operational budgets, and strict Return on Investment (ROI) targets. This is the part the industry doesn't like to sit with: If we want to truly break the social engineering attack chain , we have to stop focusing exclusively on building higher walls or executing reactive takedowns...

Enterprises are deploying AI agents faster than their identity security programs can keep pace with. Every agent that enters production carries inherited permissions, operates outside traditional IAM visibility, and makes consequential decisions without human sign-off at each step. What is agentic AI in identity security, who owns the risk it generates, and what controls actually work at scale: that's what this guide covers. Why AI Agent Risk Doesn't Behave Like Traditional Security Risk Most enterprise security models rest on a foundational assumption: a human initiates access, a policy evaluates that request, and a control either permits or denies it. AI agents break every link in that chain simultaneously. Understanding what agentic AI in identity security is requires moving beyond the surface-level observation that agents are "automated." Automation has existed in enterprise environments for decades. What makes agentic AI categorically different is autonom...

When I work on an incident, the first question I ask is almost never "what malware ran." It's "whose credentials did it use, and what was that account allowed to touch." Nine times out of ten, the interesting part of the story isn't the exploit. It's the access. The exploit gets you in the door. The privilege is what lets you walk through the building. For thirty years, that question had a human-shaped answer. A person clicked something, a person got phished, a person reused a password, an admin left a service account sitting on a domain controller with a password from 2014. The identity at the center of the incident belonged to somebody with a badge and a manager. That assumption is quietly dying, and most enterprises have not adjusted their controls to match. The actor on your network is increasingly not a person at all. It is a workload, a script, a bot, an API (application programming interface) client, and now an AI agent that can reason, plan...

Attackers prefer the path of least resistance. Why break in when you can log in? That is what makes working credentials so valuable. A leaked password, token, or API key does more than reveal sensitive data; it offers a way in. No vulnerability or privilege escalation chain necessary. If the credential is valid, the attacker can simply use it, and the session will look like ordinary activity. This shifts the questions from "where can attackers break in?" to "where do usable keys tend to accumulate? Increasingly, the answer is the developer laptop: one of the most instrumented machines most companies own, and one of the easiest places for credentials to go unnoticed. The endpoint is watched for malware, behavior, posture, and configuration. But a valid plaintext credential is something else: a door an attacker may not need to force. A fully patched fleet can still have usable keys scattered across its devices, enough to turn one foothold into something much larger....

Why Authentication Context Matters Most people think logging in is a small act. Type your password, type your code, tap a screen, scan a face, and move on. But to the systems on the other side, the method behind that moment can matter just as much as the fact that it happened at all. That is where two strangely named but surprisingly important identity concepts enter the story: OIDC's AMR and SAML 2.0's AuthnContext. They sound like the kind of acronyms that only standards committees could love, yet both were created to answer a deeply human question in digital form: How sure a system has to be before it trusts someone? The backstory starts with the internet growing up. Early online services often treated authentication as a light switch: either the user was in, or the user was out. But as online systems began handling payroll, health records, taxes, academic data, contracts, and financial approvals, that simple model started to crack. A login backed by a reused password is not ...

Cybersecurity is entering a new phase. It's one where the gap between awareness and operational execution is becoming the industry's biggest challenge. That's what stood out to me most after reviewing the results of the 2026 Bitdefender Cybersecurity Assessment , which found that organizations have never had greater insight into the risks they face, yet turning that understanding into meaningful action remains a persistent challenge. Nowhere is that gap more visible, in my view, than in how organizations handle breach transparency. We surveyed 1,200 IT and cybersecurity professionals across six countries: France, Germany, Italy, Singapore, the United Kingdom, and the United States. Respondents ranged from frontline employees to IT managers to CISOs, all working within organizations with 500 or more employees. A Governance Problem, Not an Attacker Problem One of the most troubling findings in our report is not about attacker behavior. It's about internal respons...

The most important cybersecurity impact of artificial intelligence is not that attackers can write better phishing emails or automate parts of their workflow. It is that AI is changing the speed, scale, and decision-making dynamics of cyberattacks.  That creates a problem many organizations have not yet fully confronted: most cyber governance and incident response models were designed for human-speed attacks.  For years, security teams operated under a familiar sequence. Detect suspicious activity. Investigate. Validate the threat. Escalate to leadership. Decide on containment. Communicate with stakeholders. That model still has value, but it assumes defenders have enough time to build confidence before taking material action.  AI-enabled attacks challenge that assumption.  Adversaries can now use AI to accelerate reconnaissance, generate highly personalized social engineering, modify malware, test payloads, summarize stolen data, identify vulnerabilities, a...