With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models (LLMs) and Multi-Component Protocols (MCP) - bring immense potential, but also novel vulnerabilities that traditional tools weren’t designed to handle.

Large Language Models (LLMs) are becoming integral to SaaS products for features like AI chatbots, support agents, and data analysis tools. With that comes a significant privacy risk: if not handled carefully, an LLM can ingest and remix sensitive personal data, potentially exposing private information in unexpected ways. Regulators have taken note – frameworks like GDPR, HIPAA, and PCI-DSS now expect AI systems to implement auditable, runtime controls to protect sensitive data.

Written in collaboration with Arik Galansky The GENIUS Act’s passage and the recent release of the Report from the Presidents Working Group on Digital Asset Markets reflects a pivotal shift in how the U.S. financial system is preparing for blockchain innovation.

When vulnerability scans return thousands or even millions of findings, leading to an avalanche of tickets to evaluate, the real challenge begins: figuring out what to fix first. Exposure prioritization is the critical next phase of a mature exposure management program. After defining what exposure management is and establishing a normalized foundation of aggregated data, the question becomes: how do we cut through the noise and focus on what truly matters?

11 days. That’s the global median dwell time for attackers in 2024,down from 26 days when external entities notify, but still long enough to cause significant damage. Your firewalls? They’re stopping known signatures. Endpoint tools see individual machines. But the network layer, where attackers actually move around, escalate privileges, steal sensitive data, that’s often a blind spot.