Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Building a resilient CI/CD pipeline means protecting every piece of data that makes your code run. Your environment variables, secret tokens, and configuration files demand the exact same security as your core repositories. Traditional backup protocols leave these assets completely vulnerable to silent manipulation. If ransomware subtly modifies your archived backup, executing a restore will deploy the corrupted files straight into production.

Agentic AI is rewriting the rules of enterprise risk, operating across distributed systems at a speed and scale that most security architectures weren’t designed to handle.

Security teams face a new imperative: act fast, or risk losing the vulnerability battle. The average enterprise faces thousands of vulnerabilities across a sprawling hybrid attack surface. Adversaries are using AI to discover and exploit weaknesses independently, at machine speed, making traditional disclosure timelines increasingly irrelevant. Scan-and-ticket workflows weren't built for this reality, and neither are the teams asked to execute them with finite headcount and growing board-level scrutiny.

Microsoft 365 DLP delivers real protection for regulated data in Exchange, SharePoint, Teams, and managed Windows endpoints, but only within that boundary. On-premises file servers, Linux endpoints, unmanaged devices, and non-Microsoft SaaS fall outside enforcement regardless of how policies are configured. Most security teams can't yet clearly distinguish the gaps that configuration fixes can address from those that require supplemental controls.

CVE-2026-0257 is an authentication bypass vulnerability in the GlobalProtect portal and gateway of Palo Alto Networks PAN-OS software that lets a remote attacker forge an authentication override cookie and establish an unauthorized VPN connection. The vulnerability carries a CVSS base score of 7.8 (High). It is tracked under CWE-565, reliance on cookies without validation and integrity checking. Exploitation is unauthenticated and requires no user interaction.

By rewriting our secret detection engine in Rust, we made our engine more than three times as fast. But not without making it four times slower along the way.

As more businesses switch to online operations, it becomes increasingly important to have safe, secure websites. Cyber attackers are targeting websites to steal sensitive data, demand ransom payments, and disrupt business operations. To prevent this, organizations must invest in website penetration testing. Penetration testing, also called pentesting, is a process of simulating cyberattacks to identify security gaps in a website.

Security teams are drowning in vulnerabilities. FIRST’s 2026 Vulnerability Forecast projects a median of approximately 59,000 new CVEs this year, following the 48,185 released in 2025. That is equivalent to more than 130 new disclosures each day. No team, big or small, regardless of budget, can patch all these vulnerabilities. Given no deliberate way of deciding what to patch first, organizations waste resources on low-risk findings and allow truly dangerous exposures to go unpatched.

Your last pentest probably took 2 weeks, cost 5 figures, and tested a fraction of your actual attack surface. Meanwhile, your team shipped 47 deployments in the same window, with each one almost completely untested for security. That gap between how fast you ship and how slowly you test is exactly where autonomous AI agents for penetration testing come in, especially with hackers getting smarter and faster each day (They are not using AI to summarize PDFs!).

At the World Economic Forum cyber meeting in Geneva recently, I had an interesting conversation with Vinh Nguyen, who is a strategic security advisor and Senior Fellow for AI at CFR. I wanted to know from him how he sees runtime governance in agentic AI working out practically and what approaches actually work. One of the challenges he mentioned was that yes, we need runtime governance to provide continuous and real time assurance that agents are doing what they are supposed to be doing.