The cybersecurity industry has spent decades assuming that offensive capability scales with technical expertise. That assumption is starting to break. Security teams have long estimated risk by ranking attacker sophistication. Nation-state actors sat at one end. Organized criminal groups followed. Inexperienced attackers, dismissed as "script kiddies," sat at the other end, running public

AI is moving incredibly fast, and every security leader is feeling the pressure to keep up. AI platforms like Claude, Codex and Cursor are already helping security teams write detections, investigate alerts, summarize incidents, and automate repetitive work. The conversation has evolved from whether AI belongs in the SOC, to where each type of AI delivers the most value. With so many new AI

Device code phishing - the abuse of the OAuth 2.0 device authorization grant to steal access tokens - has evolved from a niche red-team technique to an industrial-scale threat in under six months. Designed for input-constrained devices like smart TVs, printers, and so on, the device authorization login flow has been adopted by a wide range of apps and use-cases that it wasn't originally

AI is compressing exploit timelines. The real question isn't whether your vulnerability management playbook needs to change, it's which part of it you've been getting wrong all along. The conversation happening in security circles right now goes something like this: Mythos is here. Exploit timelines are collapsing. Does the vulnerability management playbook need to change? The honest answer is

Most organizations have incident response plans, security tools, and technical teams in place. Yet new research suggests that many still lack the coordination, visibility, and executive alignment needed to withstand a serious cyberattack. According to The State of Incident Response Readiness 2026, based on a survey of 600 senior IT security decision makers conducted by Vanson Bourne in January

AI agent security is moving through a familiar maturity curve: adoption, then visibility, and finally, control. But what we've collectively discovered is that enforcing least privilege for AI agents is harder than we ever imagined. This is why there are so many approaches, from prompt filtering to identity-layer access controls. Where we've collectively landed is that understanding the intent of

Most people understand identity theft as an attacker stealing a real person's sensitive information and impersonating them. Synthetic identity fraud is much harder to catch. Instead of stealing a real identity, the attacker manufactures a new one, frankensteining together several real data points with fabricated ones to create a person who doesn't exist. Since no real victim monitors misuse, a

The cycle is over. For years, cybersecurity followed a familiar pattern: defenses improved, attackers adapted, and the back-and-forth continued. Today, AI-equipped attackers are simply outpacing defenses. Most intrusions now bypass endpoint and malware-based detection entirely. The CrowdStrike Global Threat Report estimates around 79% of attacks are malware-free, as threat actors rely on

Every patch is a confession. The moment a vendor ships a security fix, the diff between the old code and the new code tells anyone watching exactly what was broken and where. Turn that diff back into a working exploit, and you can hit every system that hasn't updated yet. This is N-day exploitation, and it's always been a race: the vendor patches, the clock starts, and defenders try to deploy

Military forces are under increasing pressure to field autonomous capabilities faster than ever before. Across the U.S., UK, and NATO, new investment, evolving defense strategies, and accelerated acquisition pathways are transforming how capability is delivered, rewarding programs that can move from concept to operational deployment at commercial speed. Now the focus shifts to the trusted

More than 20 Brazilian government websites were hijacked and turned into malware delivery channels in an active PhantomEnigma campaign uncovered by ANY.RUN, a leading provider of interactive malware analysis and threat intelligence solutions. The investigation revealed previously undocumented backdoor behavior, hidden infrastructure relationships, and multiple attack arms behind a campaign

Artificial intelligence (AI) is changing offensive security, but it has not changed the standard that matters most: a finding has to be proven before it becomes useful. AI-assisted tools can read code quickly, generate payloads, summarize attack surfaces, explain unfamiliar APIs, and run repetitive testing workflows at impressive speed. That is a real advantage for security teams. It also

AI security agents are starting to influence real security decisions. They summarize findings, prioritize remediation, recommend next steps, and help teams move faster. But most still rely on fragmented risk signals: scanner output, severity scores, threat intelligence, configuration findings, and exposure data. That fragmentation matters because attackers do not move through environments one

A few days ago, I was sitting with the CISO of a Fortune 50 company, walking through how his security team was thinking about AI agents in the SOC. Smart team. Serious program. They had already connected Claude to a few detection tools and were seeing real value in specific investigations. But as we mapped out the broader architecture, something kept nagging at me. The design they were building

Most enterprises assume their asset inventory is close enough to accurate. The evidence suggests otherwise. According to a survey of over 600 security leaders in the 2026 Axonius Actionability Report, only 45% of organizations consolidate their asset and exposure data into a single view, and every downstream security program inherits whatever the inventory gets wrong. Lumen Technologies, a

AI has changed how fast attacks move. Work that once took an attacker days now takes minutes. Using models like Mythos, attackers write tailored bait, pick targets, test what lands, and jump to the next host before your team clears the first alert. That is the gap, and it is not your fault. The tools and runbooks most teams run on were built for attackers who work at human speed. AI-driven

Everyone seems to have announced a clearinghouse over the past few weeks. We did too. Ours is called Athena, and the main thing that sets it apart is that it was already real and running when we announced it — built quietly months earlier, heads down, taking findings and shipping fixes, because customers kept asking us to. We only announced it now because everyone else started announcing theirs,

A recent EvilTokens campaign targeting businesses across the US and Europe is exposing a new email security blind spot. This “ghost phishing” technique keeps the malicious page hidden until it decrypts and comes to life inside the victim’s browser. For security leaders, the risk is clear: traditional URL checks may miss the attack while Microsoft 365 access, sensitive data, and response time

For years, account takeover (ATO) followed a predictable script. Attackers bought stolen credentials in bulk, ran them through automated tools, and waited for matches. Credential stuffing was cheap, scalable, and for defenders, relatively well understood. That era is ending. Not because attackers gave up, but because the front door finally got harder to kick in. Passkeys are now mainstream.

Software supply chain security was hard enough. Then AI joined the build pipeline. For five years, "software supply chain security" meant one question: what's in your code? Which open-source packages, which versions, which transitive dependencies three layers deep that nobody chose on purpose? SolarWinds, Log4Shell, and XZ Utils all taught the same lesson: the risk lives less in the code a