Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How to Manage Identity Sprawl in the Age of AI Agents and NHIs

Non-human identities (NHIs) and AI Agents including service accounts, CI/CD credentials and cloud workload identities, now eclipse human identities in enterprise identity systems by 50:1 to 100:1. Modern identity security platforms must assign identities to these assets and furthermore, apply roles, access control policies, visibility and governance in order to secure the modern enterprise.

How to Manage Unauthorized AI Tool Usage in Your Business

In only a few years, artificial intelligence (AI) has changed almost every aspect of life, and especially so in business. Today, employees are using generative AI tools to draft emails, code software, and analyze data at lightning speed. However, there is a hidden side to this productivity boost: unauthorized AI use. Many employees are bypassing official IT channels and using shadow AI applications to get their work done.

New CrowdStrike Innovations Secure AI Agents and Govern Shadow AI Across Endpoints, SaaS, and Cloud

As organizations race to adopt new AI tools, deploy AI agents, and build AI-powered software, they create new attack surfaces that traditional security controls were never designed to protect. A key example is the prompt and agentic interaction layer, which faces novel threats like indirect prompt injection and agentic tool chain attacks.

AI vs AI: Securing the Expanding Cyber Attack Surface | Mr. Anirban Mukherji at ET Studios

In this exclusive interview byte at ET Studios, Our Founder & CEO Mr. Anirban Mukherji discusses how increasing enterprise connectivity through cloud applications, third-party integrations, and remote work is exploding the enterprise cyber attack surface making identity security and access control more critical than ever. He dives into key threats like traditional ransomware, zero-day supply chain attacks, hyper-personalized AI phishing, and systemic incidents.

Your AI Isn't Broken... Your Data Is #shorts #ai

Your AI works perfectly during testing… but suddenly fails in production. Why? The problem usually isn’t the model — it’s the data. Synthetic data looks clean and structured. But real-world data is messy: typos, missing values, broken formats, and unexpected edge cases. When AI models train only on synthetic datasets, they never learn how to handle real-world complexity. In this video, we explain why synthetic data can break AI systems and how using real production data safely can make AI more reliable.

Meet Eeva, the new video agent in the Brivo Eagle Eye VMS

The world of video surveillance is moving beyond simple recording and moving toward true intelligence. To get an inside look at our latest breakthrough in AI video surveillance technology, we sat down with Kyle Perkuhn, Sr. Product Marketing Manager at Brivo, to discuss Eeva. Unlike traditional systems which can only spot a person or a car, Eeva allows you to use natural language to define exactly what matters to your business.

The Complicating Factors of Deploying MCP in the Enterprise

Boris Kurktchiev is a Field CTO at Teleport, known for his expertise in Zero-Trust identity solutions for cloud and AI, and for his contributions to the CNCF's Cloud Native AI working group. Doyensec dropped a piece last week called The MCP AuthN/Z Nightmare, and I think anyone deploying MCP in production needs to read it.

Moonshot AI governance breakdown: Lessons from the Cursor/Kimi K2.5 incident

What happens when a $29 billion company forgets to rename a model ID, and what it means for every organization using open-source AI. On March 19, 2025, Cursor, the AI-powered coding tool valued at $29 billion and generating an estimated $2 billion in annual recurring revenue, launched Composer 2, its newest and most powerful coding model.

Why NER models fail at PII detection in LLM workflows - 7 critical gaps

In AI systems, PII detection is the first step. Not the most glamorous step. But the one that, when it fails, takes everything else down with it. Identifying sensitive data (names, Social Security numbers, financial records, health information) has to happen before any of it reaches an LLM. Get this wrong, and you’re looking at one of two bad outcomes: Traditional DLP systems could afford to be aggressive with detection. LLMs can’t. They depend on full context to generate correct outputs.