Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI security threats are evolving at roughly the same speed that AI itself is: extremely fast. One of the most recent—and least understood—vulnerabilities involves vector and embedding weaknesses. These issues have gained attention with their addition to the OWASP Top 10 for LLMs, and the risks are becoming more urgent as Retrieval-Augmented Generation (RAG) continues to dominate enterprise AI adoption.

A practical guide for chief AI officers and technology leaders implementing federal AI governance The US Office of Management and Budget's recent memoranda — M-25-21, "Accelerating Federal Use of AI through Innovation, Governance, and Public Trust," and M-25-22, "Driving Efficient Acquisition of Artificial Intelligence in Government" — establish comprehensive frameworks for federal agencies that implement AI systems while maintaining appropriate safeguards.

As stated in my recent blog, CISO: Chief Cybersecurity Warrior Leader, I define warrior-leadership as a term that combines the concepts of leadership and expertise in the field of cybersecurity with the mindset and traits of a warrior. A warrior mindset helps leaders remain calm, focused, and effective in addressing and resolving complex challenges.

Let’s cut the fluff out of cloud security. As you build and innovate in the cloud, you create a maze of roles, permissions and resources that you must secure thoughtfully. The dirty secret is that as organizations launch and build new infrastructure, they also create a labyrinth of permissions that attackers can exploit if they get their hands on a valid password or credential.

We’re excited to announce a new integration with StackHawk—a developer-first DAST platform that brings runtime vulnerability testing directly into CI/CD workflows. With this integration, StackHawk findings are now ingested directly into Jit’s unified product security backlog—right alongside SAST, SCA, CSPM, secrets detection, and more.

When you unify a complex technology landscape that consists of diverse deployments—including on-premises, legacy systems, multi-cloud and hybrid environments—you inevitably create a tangled web of identities, each with its own set of security challenges. Identity and access management (IAM) as we know it struggles to keep up. It becomes increasingly complex to manage user access across disparate systems, to maintain consistent security policies and to meet compliance regulations.

Autonomous AI agents - known as Computer-Using Agents (CUAs) - are no longer science fiction! These systems can browse websites, interact with applications, and carry out tasks on their own. While intended to increase productivity, they can already be repurposed by threat actors for malicious use.

Let’s start with an obvious statement, and then let’s dig into it. Security incident investigations are expensive. Period. Especially when multiple highly-skilled team members are involved. Every hour spent hunting down threats or false alarms carries a real dollar cost. Industry research shows that the fully-loaded labor rate for IT security staff averages about $62.50 per hour.

In DNS spoofing, hackers will modify DNS records that are directing web traffic in a way that users will now be sent to fraudulent websites that may seem legitimate. Basically, DNS is the internet's phone book. However, DNS can be tricked. In DNS spoofing, the cybercriminals place false DNS records in the DNS server cache, which resolves and directs users to websites controlled by the attacker.