Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Shift left. Secure fast. Release often. Mobile teams are adopting this approach from day one to boost productivity, facilitate cross-team collaboration, and shorten release cycles. As mobile apps become the primary gateway to business and customer data, embedding security into every stage of development isn’t a luxury—it’s a necessity.

Your AI Agent just updated a vendor’s payment details in your Enterprise Resource Planning (ERP) system based on a seemingly harmless prompt. No data was exfiltrated. No access policy was violated. But now, a $250,000 payment is sitting in a fraudulent bank account. This is the new face of AI risk. As enterprises adopt AI Agents - either off the shelf or custom built, security teams are facing a fast-moving shift.

Threat Intelligence (TI) has become the secret weapon of modern security teams—essential for identifying possible emerging threats before they escalate. But TI is only as valuable as its accuracy, relevancy, and timeliness. Unfortunately, many traditional TI approaches can no longer keep up, as security teams are plagued with information overload: too many signals, too little context, and limited resources to process everything. This is why the coupling of GenAI and TI is a game changer.

The increasing deployment of large language models (LLMs) in enterprise environments has created a pressing need for effective security testing methods. Traditional approaches, relying heavily on predefined templates, are limited in comparison to adaptive attacks — particularly those related to prompt injection attacks. This limitation becomes especially critical in high-performance computing environments where LLMs process thousands of requests per second.

Nestled in a log cabin high in the Rocky Mountains, Rick Bohm starts his day the same way he’s approached his career: intentionally, with a quiet commitment to learning and action. Boasting more than three decades of cybersecurity experience, Rick has watched tech evolve from dial-up ISPs to advanced AI-driven security architectures – and through it all, he’s focused on one enduring mission: protecting data, organizations, and people.

When we first launched Egnyte’s AI features built on retrieval-augmented generation (RAG), customer response was overwhelmingly positive. Users could quickly find and synthesize information from vast document repositories with accuracy and context. But success breeds ambition. As customers grew comfortable with the system, they began exploring new use cases that revealed a limitation: while our RAG excelled with plain text, it struggled with tables, charts, and other structured formats.