In today’s modern networking environment, administrators in many organizations face the challenge of managing authentication for employees, especially when dealing with large staff numbers. Granting access to individual tools can be tedious for both administrators and users, as employees must log in multiple times when switching between tools and remember separate credentials for each.

As organizations increasingly integrate artificial intelligence (AI) into their operations, the nature of data security is undergoing significant transformation. With AI’s ability to process vast amounts of data quickly, the risk of data breaches and leaks has grown exponentially. In this context, Data Loss Prevention (DLP) has (re)emerged as a critical component for IT professionals seeking to safeguard sensitive information.

On September 11, 2024, GitLab released patches for a critical vulnerability affecting various versions of GitLab CE/EE, identified as CVE-2024-6678. This flaw allows a remote attacker to trigger a pipeline as an arbitrary user under specific conditions. A GitLab pipeline is a collection of automated processes that run in stages to build, test, and deploy code.

In today's digital landscape, cyber attacks are an ever-present threat, and they all ultimately target one thing: data. For most organizations, the challenge lies not only in protecting this data but also in understanding the full scope of what they have. Many organizations struggle to identify how much sensitive data they possess, where it resides, and who has access to it.

One of the primary reasons that the Bitsight Security Rating is widely respected and closely correlated with real-world security outcomes is the scale and sophistication of our asset attribution capabilities. In a recent post, my colleague Francisco Ferreira shared an update on the momentum building with Bitsight Graph of Internet Assets (GIA), the AI-powered engine we use to map assets to organizations and build our Ratings Trees.

As businesses increasingly adopt Generative AI (Gen AI) to enhance operations, customer engagement, and innovation, the need for robust AI guardrails has never been more critical. While Gen AI offers transformative potential, it also introduces significant risks that can jeopardize your business if not properly managed. Below, we explore five critical risks associated with Gen AI and provide strategies to avoid them.

AI guardrails are vital for ensuring the safe and responsible use of AI/large language models (LLMs). However, focusing solely on single prompt-level checks can leave organizations vulnerable to sophisticated threats. Many company policy violations and security risks can be cleverly split across multiple, seemingly innocent queries. To effectively protect against these threats, a more comprehensive approach is needed — session-level monitoring.

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Admission control is a crucial part of the Kubernetes security, enabling the approval or modification of API objects as they are submitted to the server. It allows administrators to enforce business logic or policies on what objects can be admitted into a cluster. Kubernetes RBAC is a scalable authorization mechanism, but lacks the fine grained control over different Kubernetes objects. This creates the need for another layer of control which is Admission Policies.