There is a lot of panic around Claude Mythos. Some people are saying it will hack every system, that the sky is falling, and that there is no stopping it. That fear is dangerous because it makes teams freeze. Claude Mythos is genuinely powerful. AI systems like this can find security issues in minutes that even experienced penetration testers might take weeks to identify and exploit. That part is real. But here is the important point: AI is still exploiting what is already there.

Attack Surface Management (ASM) is the continuous process of identifying, analyzing, and reducing security exposures across an organization’s environment. It provides visibility into assets, highlights security gaps, and helps prioritize remediation based on real risk.

Researchers at Google’s Threat Intelligence Group (GTIG) are tracking a new threat actor that’s impersonating help desks to trick users into installing malware. The threat actor, which GTIG tracks as “UNC6692,” begins by sending a large volume of spam emails to the victim, then initiates contact via Microsoft Teams to ostensibly help the user block the spam.

Most “hardening” advice for AI agents is a checklist of things to configure before the agent runs. CIS Kubernetes Benchmark gates. Pod Security Standards baselines. NetworkPolicy templates. None of it’s wrong — it’s just one of four phases, the one your stack already covers. The other three are Observe, Enforce, and Reconcile. They’re where AI agents actually get breached, and they’re where most stacks have nothing.

Drupal powers over 1.7 million websites worldwide and is the CMS of choice for teams that need strong security and flexibility. Meanwhile, Salesforce, with a 20.7% share of the global CRM market, is trusted by more than 150,000 businesses, including 90% of Fortune 500 companies. Most organizations that reach a certain scale end up using both. And that is exactly where things get complicated.

Most enterprise data security tools are built for a world where IT owns and manages every device. That world no longer exists. Contractors work from personal laptops. Entire teams run ChromeOS. Frontline workers access corporate systems through shared or unmanaged devices. And every one of those browser sessions can involve uploads, downloads, copy-paste, and form inputs touching sensitive data.

With the rise of data breaches and hacking attempts, a strong cybersecurity posture is the most significant need today. Given the scale of cybercrime growth, you need to carefully consider several key factors that will ultimately impact the cybersecurity solution you pick. Businesses have realized the value of their data; now they must invest in tools to easily detect and respond to security issues.

Every AI workload security PoC reaches the same conversation. Platform engineering pushes back: the AI team won’t accept extra latency on inference. The security engineer hunts for benchmarks and finds a contradiction. Langfuse publishes 15% overhead. AgentOps publishes 12%. The security vendor quotes 1–2.5%. None is lying. They measure different layers.

Anyone who's stood up a SIEM from scratch knows the feeling: weeks of infrastructure work, integration headaches, and a services team alongside for the whole process. That experience shaped how people think about adopting anything new in security ops. The instinct is to treat AI the same way: budget for it, plan for it, bring in specialists. This instinct is costing teams real time. Traditional infrastructure takes great effort to stand up. Infrastructure-as-code happens in seconds.