The Hacker News | Expert Insights

We recently worked with an organization that had invested heavily in advanced security tooling, including AI-driven detection and monitoring capabilities. From a technical perspective, the environment appeared mature: alerts were firing, dashboards were populated, and risks were clearly identified.  Yet progress had stalled.  The security team and IT disagreed on ownership. Business leadership perceived cyber risk as “under control,” while the security team felt increasingly exposed and unheard. AI surfaced the signals, but no one could agree on what to do with them.  The turning point did not come from additional tooling or deeper analysis. It came from reframing the conversation.  By aligning stakeholders around clear business impact, contextualizing the findings against industry peers, and translating technical gaps into credible, board-level risk narratives that reinforced the internal security team’s concerns rather than questioning their judgment, decisions were finally ma...

While working on the Transparent Tribe’s vibeware research, we have encountered two distinct camps, the optimists and the skeptics. What makes the current dialogue unique is that both sides can be right at the same time. There is, however, a clear operational reason why we encounter "AI attacks" primarily on professional social media feeds rather than within our own telemetry logs. In this article, we analyze the factors explaining why Skynet is not here yet, and how, much like a shark, AI does not need to be innovative to be dangerous. LLM Architecture Bias LLMs are mathematically optimized to predict the most likely outcome, while hacking is the art of identifying the statistical anomaly. LLMs are designed to predict the most statistically probable next token. They are excellent at the average, but poor at the exceptional. A hacker, by contrast, is a practitioner of statistical anomaly, actively seeking the low-pro...

Vulnerability management is the continuous process of identifying, assessing, prioritizing, and addressing security weaknesses across systems, applications, and infrastructure. It extends beyond periodic scanning; it includes validating findings, understanding exposure in real-world environments, and tracking remediation over time. Effective vulnerability management combines asset visibility, vulnerability intelligence, and operational context to determine which flaws present actual risk rather than theoretical exposure. Modern IT environments further complicate the process of vulnerability management. Hybrid IT infrastructure, third-party dependencies, and internet-facing services increase the attack surface while generating large volumes of vulnerability data. Security teams must balance operational constraints, such as out-of-support legacy systems and uptime requirements, with the need to quickly reduce exposure. As a result, vulnerability management is no longer limited to coun...

Most application security (AppSec) teams know their OWASP Top 10, the industry-standard list of the most critical software security risks. Fewer know which of those categories their organization actually fixes. In conversations with security teams, I hear the same story: "We prioritize criticals, so the important stuff gets handled." The data tells a different story. Fix rates vary dramatically by OWASP vulnerability class, and not in the ways most teams expect. The data comes from Semgrep's Remediation at Scale report , which analyzed anonymized remediation patterns across 50,000+ repositories and hundreds of organizations during 2025. The methodology is straightforward: group organizations into two cohorts by fix rate (top 15% as "leaders," remaining 85% as "field"), then compare what each group actually does differently. The gap between leaders and the field isn't about detection quality or prioritization frameworks. Both cohorts apply the s...

While AI reduces some coding flaws, credential sprawl accelerates, expanding the non-human identity attack surface, and making remediation the new security bottleneck. AI is changing software development faster than most security teams can adapt. As coding assistants and autonomous agents become embedded in daily workflows, many assume traditional application security controls will steadily lose relevance. If machines can scan code, catch flaws, and even suggest safer alternatives in real time, then software risk should start to shrink. But that’s not what is happening in the real world, according to GitGuardian’s security research. The battle isn’t in the code anymore, because AI is shifting where the control point is. It’s in the credentials, tokens, service accounts, and machine identities that AI systems need in order to access data and take action. This matters because the attack surface has fundamentally changed. AI-assisted commits grew exponentially in 2025 and leaked secr...

Telegram entered 2025 under unprecedented pressure. Public scrutiny, regulatory attention, and leadership turmoil forced the platform to do something it had long resisted, enforce at scale. Moderation volumes surged, automation expanded, and millions of channels and groups were removed in a single year. On paper, this looks like a turning point.  In practice, it wasn’t the collapse of cyber criminal activity on Telegram; it was an evolution, for sure, but not a collapse.  What we are seeing in 2026 is not a mass exodus from the platform, nor a meaningful decline in threat actor coordination. Instead, Telegram’s crackdown has triggered a familiar pattern. Criminal ecosystems adapt faster than platforms can reform. Read the just released Telegram report, by Tal Samra and Or Shichrur for evasion methods, statistics and monitoring recommendations: https://checkpoint.cyberint.com/telegrams-crackdown-criminal-resilience Over 43 Million & Channels Blocked  Tele...

Higher education institutions operate some of the most complex identity environments of any industry. Universities often struggle to balance open access for learning and research with strong security controls to protect students, faculty, and sensitive institutional data. This contrast creates unique identity security and management challenges that require specialized strategies and tools. A Highly Complex Identity Ecosystem Unlike corporate businesses, the ecosystem that is common at universities requires them to manage a variable and highly diverse population of users. Churn is a constant challenge, with students, faculty, alumni, researchers, contractors, and affiliate colleges and labs, and other contributors enrolling, changing roles or status, tracks or departments, and leaving frequently.  This dynamic ecosystem results in an identity lifecycle that is far more fluid than most corporate environments. “Joiners”, or new identities, are created continuously, while “movers a...

When Shai-Hulud 2.0 hit in late 2025, it was a brutal, expensive wake-up call for DevSecOps teams. It showed that the industry's direction of shifting left, where teams pass security onto developers, wasn't the silver bullet everyone hoped for. Pushing that responsibility was fine in theory, but it crumbled quickly because the foundation it was built on was inherently flimsy. As we move further into 2026, we need a more definitive fix to the structural weakness in the pipelines in light of a potential Shai-Hulud 3.0. A major lesson from 2.0 was that internal CI/CD runners were easily hijacked and turned into attack botnets. Teams need to take that finding and come back with a truly proactive defense. A curated catalog is a way for security teams to control exactly what code and components enter their environment, while still giving engineering teams a fast, secure way to build - it is the key to creating a sustainable solution. More on a curated catalog later. The Anatomy o...