Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your RAG implementation can expose secrets in some unexpected ways. Secure your LLM deployments and scrub knowledge bases to prevent your secrets from leaking.

AI agents are evolving fast — from helpful assistants to autonomous actors that can browse the web, analyze data, resolve customer service issues, assist in generating code, book travel, and more. As these agents take on more responsibilities, it’s crucial that the security model around them keeps up.

It’s been a year since we announced 1Password Extended Access Management, and in that time, it’s become clearer than ever that we are facing a major shift in how workers use technology to drive productivity. Whether it’s through organizations embracing the use of AI agents or tech-savvy employees independently seeking out any tool or application they need, the way we work has fundamentally evolved. And cybersecurity must evolve with it.

I think you’ll agree with me that growth in the AI landscape is pretty full-on at the moment. I go to sleep and wake up only to find more models have been released, each one outdoing the last one by several orders of magnitude, like some kind of Steve Jobs’ presentation on the latest product release, but on a daily loop. With these rapid developments, security must keep up or it will be left behind.

The rapid adoption of generative AI (GenAI) in the enterprise is introducing a new category of unmanaged risk known as shadow AI. Organizations frequently lack insight into which employees are using GenAI tools and how they are being accessed, resulting in visibility limitations, policy enforcement challenges, and increased risk of data exposure. Security teams face potential data leaks and compliance violations, while IT teams struggle to integrate GenAI usage into existing governance models.

MCP, short for Message Communication Protocol, refers to a category of protocols used for exchanging structured messages between systems or applications. It was developed primarily to meet the communication needs of early enterprise systems that required: MCP protocols are often seen in banking, insurance, healthcare, and telecom industries—sectors where many systems were developed before APIs became mainstream.

Generative AI has gone from a novelty to an essential part of daily workflows across all teams at an organization. Whether it’s ChatGPT, Microsoft Copilot, Claude, or Google Gemini, employees are using chatbots to copy, paste, summarize, and query data at a pace and scale we have never seen before. Unfortunately, data security has not been a fundamental feature of generative AI as the technology’s popularity and functionality has exploded.