Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

On April 8, 2025, the National Security Division (NSD) of the US Justice Department (DOJ) implemented the Data Security Program (DSP).

In this episode of Security Matters, host David Puner welcomes back David Higgins, senior director in CyberArk’s Field Technology Office, for a timely conversation about the evolving cyber threat landscape. Higgins explains why today’s attackers aren’t breaking in—they’re logging in—using stolen credentials, AI-powered social engineering, and deepfakes to bypass traditional defenses and exploit trust.

Application Security, WAF, and OWASP form an interconnected defense strategy for web applications. OWASP (Open Web Application Security Project) provides the framework for identifying critical vulnerabilities through resources like the OWASP Top 10, while WAFs act as the protective layer that detects and blocks attacks targeting these vulnerabilities in real-time.

In times of geopolitical and economic instability, no organization would consider running without backups, additional support, clear end goals, and company-wide communication. Within business, the wisdom of strength in numbers and power in unity is widely understood. However, when it comes to its cybersecurity, a critical pillar that reputation, safety, and resilience rely upon, the opposite often happens.

Cilium network policies (CNPs) extend Kubernetes’ L3/L4 controls to the application layer (L7). CNPs provide teams with advanced networking capabilities, but they can also introduce new ways for connectivity to fail, especially in environments running thousands of workloads. Many of these issues stem from differences in how Kubernetes and Cilium interpret the same concepts, such as label scoping, IP-based rules, service identities, and how default-deny behavior is applied.

The Monthly Intelligence Insights report provides a summary of top threats curated, monitored, and analyzed by Securonix Threat Labs in November 2025. The report also includes a synopsis of the threats, indicators of compromise (IoCs), tactics, techniques, and procedures (TTPs), and related tags. Each threat has a comprehensive summary from Threat Labs and search queries from the Threat Research team.

As organizations accelerate the adoption of Artificial Intelligence, from deploying Large Language Models (LLMs) to integrating autonomous agents and Model Context Protocol (MCP) servers, risk management has transitioned from a theoretical exercise to a critical business imperative. The NIST AI Risk Management Framework (AI RMF 1.0) has emerged as the standard for managing these risks, offering a structured approach to designing, developing, and deploying trustworthy AI systems.