Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Kovrr's cyber risk quantification (CRQ) models are built on a continuously updated database of real-world cyber events, drawing on regulatory disclosures, company filings, legal reports, and proprietary insurance claim intelligence to produce financial exposure estimates grounded in how incidents actually unfold. That intelligence foundation has always informed everything the platform produces, from frequency and severity calculations to the event catalogs that drive each organization's quantification.

Prompt injection is among the defining security challenges of the AI era. As organizations move from chatbots to AI agents, adversaries are finding more ways to manipulate the language, context, and data these systems trust. With the rise of powerful AI agents that can crawl webpages, access file stores, and even write shell commands, indirect prompt injection has emerged as a critical threat vector.

Most organizations that have invested in AI governance have done so without first solving the problem that makes governance possible in the first place: knowing what AI they are actually running. An AI governance program built on an incomplete inventory is governing a partial picture of actual exposure. ‍ The risks concentrated in the AI systems that never made it into the formal catalog are not lower priority because they were not captured. They are simply invisible, which is considerably worse.

Security teams are approving large language model (LLM) deployments faster than they can build the controls necessary to govern them and protect vital, sensitive data. Employees paste customer records into ChatGPT, engineering teams connect internal APIs to coding assistants, and business units stand up retrieval systems against production data, often without formal review.

Cramming for an exam in school often meant late nights, large quantities of caffeine, and anxiety about potential final grades. Whether studying alone or as part of a group, you probably tried to pull together all your notes and review sheets, so you had the right information at your fingertips during the test. In some cases, these exams could determine whether you passed or failed a course.

Fraudsters are adaptive actors who follow the path of least resistance. The goal of fraud-fighting teams is often to make the cost of fraud high enough that attackers go elsewhere. Leveraging Persona’s Mobile SDK raises fraudsters’ costs by requiring identity verification to happen in signal-rich mobile environments. For years, fraud fighters have layered verification checks and risk signals to detect various types of attacks.

KeeperDB is a secure, multi-protocol database client built on Keeper’s zero-knowledge platform. Available as both a free standalone desktop application and a privileged session component of KeeperPAM, KeeperDB combines a database query engine, vault-managed credentials, real-time performance monitoring and an AI-powered database assistant in a unified interface. Continue reading to learn more about how KeeperDB works, its key features and the benefits of using it to help secure database access.

88% of organizations are running AI in at least one workflow, yet nearly two-thirds report more rework than savings. The model is rarely the bottleneck. Everyone has access to the same frontier models now. The difference is what sits underneath: content that's unstructured, ungoverned, and disconnected from the workflows that need it. Fixing the content problem usually means giving AI broad access to content, and that's where governance breaks down.

At Corelight Labs, our mission is to help organizations stay a step ahead of evolving threats. When our researchers came across Censys' detailed write-up on ScoutC2, a rapidly growing open-source command-and-control (C2) framework favored by threat actors, we knew we needed to bolster community defenses quickly.

In Part 1 of this series, we addressed 18 Kubernetes best practices spanning across container hardening, observability, availability, and fault tolerance. Those practices secure the containers that agents run in. But the CNCF AI Technical Community Group's cloud-native agentic standards go further, establishing that securing containers is only the beginning.