Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A few practical improvements this week, mostly driven by what we’re learning as customers deploy CertKit into larger infrastructures.

The OWASP Top 10 for Agentic Applications defines the most common AI agent risks, but real attacks unfold across multiple stages of behavior. Behavioral analytics detects those risks by modeling how users, AI agents, and their interactions change over time. By observing deviations across inputs, processing, and outputs, security operations teams can identify insider‑driven and agent‑driven threats that traditional, event‑based detection misses.

Picture the first meeting after a serious security event. The Security Operations team is talking about alerts, detections, and lateral movement. Threat Intelligence is talking about adversary tradecraft and known campaigns. Governance and Risk is talking about control gaps, exposure, and business risk. And leadership? They only care about how bad this event is, and what the team is doing about it. Security teams often agree on the mission: deter and stop threat actors at all costs.

OWASP Penetration Testing is a specialized type of security testing that focuses on attack vectors and vulnerabilities listed in OWASP Top 10. An organization’s security landscape is complex, and thus it is essential to test the organization’s security measures to ensure that they are working correctly. OWASP’s (Open Web Application Security Project) compiled a list of the top 10 attacks named OWASP Top 10 for multiple technologies such as Web Applications, Cloud, Mobile Security, etc.

On average, Astra Security detected 5.33 vulnerabilities per minute in 2025, which is more than 7,000+ vulnerabilities per day in live environments. That’s the brutal math of the Modern attack surface. Without proper pentesting, each deployment cycle introduces multiple entry points for hackers, and each overlooked endpoint increases the risk of cyberattack.

If you are not using AI to defend against AI, you will lose. But for organizations operating in air-gapped environments, the path to AI-driven defense can be blocked by the very isolation that protects them. Today, we're announcing that Elastic Security is now the embedded security layer for Google Distributed Cloud (GDC) air-gapped environments, expanding our collaboration with Google Cloud.

As AI adoption accelerates on Google Cloud, the challenge for most teams today is no longer just building AI-powered applications. It’s also managing the full AI stack from end to end, including data pipelines, infrastructure, release process, and security operations. Many teams are monitoring these layers with different tools, creating complexity, fragmenting visibility, and slowing decisions on what to do next.

Cloud environments create a flood of security signals, often reaching tens of thousands per day depending on the organization’s size. Security engineers and analysts spend a disproportionate share of their time triaging these signals instead of acting on legitimate threats. But the time-intensive parts of that work, such as identifying related signals and building a timeline, can be handled systematically, leaving teams free to focus on what actually requires human judgment.

A patient calls your privacy office and requests an accounting of every disclosure of her PHI made outside treatment, payment, and healthcare operations over the past six years. This is her right under HIPAA. Your privacy officer pulls the EHR disclosure log. It is complete through the day your organization deployed its first production AI agent.

A CISO at a mid-sized SaaS company pulls her platform lead aside after a board meeting. One question: “Do we have AI agents running in production?” The lead pauses. He knows the data science team has been experimenting with LangChain. He remembers a conversation about a customer-support pilot. He thinks there might be an inference server in staging that got promoted last quarter.