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

Despite the abundance of telemetry at analysts’ disposal, many security operations teams struggle to answer a few basic questions during incident investigation: What happened? What evidence do we have? How do we know we’re seeing it all, in context? Answering these questions requires teams to go beyond alerts, the most common basis for initial triage. But investigations (and their outcomes)

We are standing at the end of an era we never thought to mourn: the era of human-speed threats. For years, cybersecurity moved to a rhythm organizations could follow. A researcher found a bug, a CVE was cataloged, a vendor navigated a patch cycle, and weeks or even months later, a fix was deployed. In this era, dwell time was measured in days, sometimes weeks. We are now approaching an

Every weapon begins as an extension of the hand that holds it. The spear lengthened the reach of the arm. The bow sent the point flying without the throw. The rifle placed a man's death a quarter mile beyond his sight, and the aircraft carried that death across oceans. At each turn, the distance between the warrior and the wound grew wider, and yet one thing never moved: a human chose the target

Earlier this month, I spoke at the Gartner Security & Risk Management Summit about a blind spot most security programs are still not accounting for - how attackers are circumventing AI security programs by using legacy infrastructure to hijack AI agents. AI adoption is moving faster than security programs can account for. Roughly 71% of organizations are piloting AI agents across their

Most good security work is invisible by design. Today is the exception. The 2026 Cybersecurity Stars Awards winners are announced across 95 subcategories in four main award categories. The reason is simple. Cybersecurity is full of work that deserves recognition and rarely gets it. Products that quietly close real gaps. Teams that stop incidents nobody reads about. Companies that raise the

For thirty years, vulnerability management ran on a buffer: the months between when a vulnerability was found and when someone could figure out how to weaponize it. The solution was straightforward enough; triage by severity, schedule the fix, validate, and move on. The buffer was what made that work. Today, that buffer is gone. AI didn't make your team slower. It changed the other side of the

Your pentest report looks clean. That might be the problem. Run automated pentesting long enough, and the new findings start to dry up. By the third or fourth run, fewer issues appear. The report looks stable. Leadership reads "stable" as "secure." It usually isn't. The work slows down. The risk does not. That gap is what a The Hacker News webinar with Picus Security sets out to close. Autumn

Organizations have more visibility than ever. Growing tech stacks provide greater coverage, and network security teams are increasingly adopting AI and automation to help with routine tasks and reduce manual effort. But the same challenges persist. Outages still last hours, causing significant financial losses, operational disruption, and reputational impact. Threat response and mean time to

Phishing has always been a numbers game. AI has turned it into a volume machine. Attackers can now create convincing emails, fake login pages, and tailored lures in minutes. Every polished message adds another case for Tier 1 to review, another link to inspect, and another alert that cannot be dismissed at a glance. As the queue grows, a credential theft attempt or malware delivery can easily

Mythos is real. I know a big chunk of the industry thinks it's a marketing stunt, and I get why. I get it. But I've seen the findings, and they're bad. These aren't "whoops, this line right here is wrong, and that's RCE." They're novel combinations of a few dozen issues out of thousands of things every SAST scanner already finds, chained together into something much worse. It's real creativity,