Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Risk in the agentic enterprise spans people, service accounts, and AI agents. All three act with legitimate credentials, so access controls alone can’t show whether their activity is safe. A behavioral risk framework applies one model to every actor: Verify expected behavior before access goes live, observe activity once it does, analyze behavior for risk and drift, and improve detection coverage over time.

The recent disclosure of a major vulnerability in Citrix NetScaler pointed to a widespread weak point: a login page is an attack surface that can be used to compromise and access an application server. Indeed, the disclosures included an actively exploited flaw that allowed attackers to execute commands on affected gateways without signing in. As with any emergency patch, admins had to weigh cutting off or disrupting their users’ access with the risk of delaying a critical security update.

Researchers pulled real, leaked API keys out of three LLMs using only API output. Here is how AI agents carry secrets from developer laptops into model context, and how to stop them before the model sees them.

As AI agents spread across IT environments, organizations struggle to see what agents they have and what data those agents can access. For Cybersecurity Awareness Month, we wanted to share what Netwrix is doing to help IT and security teams improve agentic AI visibility and governance to keep their sensitive data secure. I recently sat down with Jeff Warren, our chief product officer, who has been working through this problem with customers for a while.

RevStealer is an infostealer first observed in September 2026 that targets Windows systems, with reported 64-bit execution and JavaScript-based components. It is built to steal authentication credentials and other sensitive user data while emphasizing stealth through anti-debugging, anti-sandbox, anti-VM, and evasion features. Its threat significance comes from a short, theft-focused execution model that pairs broad data collection with concealed command-and-control and exfiltration behavior.

AI guardrails are sold as a solution to keep models behaving as intended - but it can be hard to figure out what you’re getting. The problem starts with the word itself. "Guardrails" means different things depending on who's selling them. Some vendors use it for simple prompt filtering, others bundle in entirely different controls and call it "guardrails" because the word sounds reassuring. Without a shared definition, buyers can't easily tell what they're actually getting.