Mythos changes the speed of attack. Identity controls decide what happens after. The shift underway For the first time in 19 years, vulnerability exploitation now leads the Verizon Data Breach Investigations Report as the breach entry point. It accounts for 31 percent of incidents, ahead of stolen credentials. Threat actors are using AI to exploit known vulnerabilities in hours rather than months. The Verizon data predates the latest frontier AI advancements.

The cybersecurity industry is entering a new phase of AI adoption. Frontier AI models are increasingly capable of identifying vulnerabilities, investigating threats, analyzing code, and accelerating security operations at machine speed. At the same time, innovation is moving rapidly. New models, platforms, and security-focused AI initiatives are emerging across the market, each pushing the boundaries of how AI can be applied to real-world cybersecurity workflows.

Globally, companies are still preparing for a quantum revolution, and the National Institute of Standards and Technology (NIST) has made significant progress on its Post-Quantum Cryptography (PQC) Standardization Program.

Google recently released Device-Bound Session Credentials (DBSC) for Google Chrome and Google Workspace. It is a long-awaited new security enhancement to fight back against local cookie theft. But, yes, it can still be hacked and phished. Nothing alone in cybersecurity is a complete panacea.

AI agents can investigate a single vulnerability brilliantly, but that is only about 20% of vulnerability remediation. This post breaks down the other 80%: the data normalization, cross-tool asset identity, SLA enforcement, exception governance, and audit evidence that turn individual agent outputs into a governed, provable remediation program, and why AI and a platform like Seemplicity work better together than apart.

Researchers at Malwarebytes warn that a fake ChatGPT download site is delivering malware. The attackers use sponsored results and SEO manipulation to target users who search for “ChatGPT download.” The phishing page is a convincingly spoofed version of the legitimate ChatGPT website, which delivers malware tailored to Windows or Mac users.

Your team already knows the pattern. The on-prem SIEM is still running, but it's become a bottleneck instead of a force multiplier. Cloud logs arrive late or in partial form. SaaS activity sits in separate consoles. Endpoint and identity events don't line up cleanly. Analysts burn time pivoting across tools, then still end up asking whether the alert is real. That's why the conversation around SIEM on cloud has changed. It's no longer about chasing a newer deployment model.

Your source code passed every scan. Every code review approved. Every linter ran clean. Your users just downloaded the compiled binary. Those are not the same artifact. Code-centric SAST tools analyze the code you write. Appknox analyzes what you ship. This is not a feature distinction. It is an architectural one, with direct consequences for what gets caught and what does not.