Linx has built an AI-native platform that maps, monitors, and governs human, non-human, and agentic identities across the entire enterprise environment, relying on real-time detection and automated remediation to reduce identity-related risks.
Hasbro detected an intrusion on March 28, prompting the company to take down some of its systems. Parts of Hasbro's website appeared down, with error messages indicating maintenance.
"For healthcare, government, and contact center environments, reducing risk at the endpoint is essential. By aligning IGEL's immutable endpoint OS and Adaptive Secure Desktop™ with Windows 365 and Microsoft Azure Virtual Desktop, these reference architectures give organizations clear guidance for delivering secured and resilient digital workspaces."
Rhyne's attack involved unauthorized remote desktop sessions, deletion of network administrator accounts, and changing of passwords, showcasing significant security vulnerabilities.
TruRisk is designed to aggregate vulnerability data at the asset level and convert it into a measurable, business-aligned cyber risk score. Rather than evaluating vulnerabilities in isolation, TruRisk calculates a consolidated risk value per asset by helping security teams understand which systems pose the greatest operational and strategic risk.
Unverified and low quality data generated by artificial intelligence (AI) models - often known as AI slop - is forcing more security leaders to look to zero-trust models for data governance, with 50% of organisations likely to start adopting such policies by 2028, according to Gartner's seers. Currently, large language models (LLMs) are typically trained on data scraped - with or without permission - from the world wide web and other sources including books, research papers, and code repositories.
Only 22 percent of organizations treat AI agents as independent, identity-bearing entities, while 88 percent have already dealt with suspected or confirmed security incidents involving AI agents. Ninety percent of AI usage occurs through unauthorized personal accounts, with an average of 223 shadow AI incidents per month.
Zero trust is not a thing; it is an idea. It is not a product; it is a concept - it is a destination that has no precise route and may never be reached. But it is described very succinctly: trust nothing until the trust is justified. Justification starts with verifying every subject's identity and authority. This is the single constant in all zero trust journeys: they start with the subject's identity. Zero trust's reliance on identity, and identity's reliance on AI Two questions. Can you have zero trust without effective identity verification? No. Can you have effective identity verification in the age of AI? Maybe, and maybe not.
Near-identical password reuse occurs when users make small, predictable changes to an existing password rather than creating a completely new one. While these changes satisfy formal password rules, they do little to reduce real-world exposure. Here are some classic examples: Adding or changing a number Summer2023! → Summer2024! Appending a character Swapping symbols or capitalization Welcome! → Welcome? AdminPass → adminpass Another common scenario occurs when organizations issue a standard starter password to new employees, and instead of replacing it entirely, users make incremental changes over time to remain compliant.
