Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Cloud data security solutions protect sensitive data across SaaS, IaaS, and hybrid environments, covering discovery, classification, access governance, DLP, and evidence for compliance. No single tool covers everything. The right stack depends on where regulated data actually lives, who has access to it, and what evidence your compliance team needs to satisfy auditors. Regulated data doesn't stay in one place, and cloud data security solutions need to account for that reality.

Originally published in Vancouver Tech Journal, June 2, 2026. Bijan Sanii is CEO and founder at INETCO It may seem reassuring that JPMorganChase, the largest U.S. bank, is among the 12 launch partners involved in Anthropic’s Project Glasswing. But given the stark cybersecurity warning the initiative represents, including a single financial institution is nowhere near enough.

The conversation around AI in cybersecurity is changing. The first question was whether AI could help security teams move faster. It can. AI-led security operations can accelerate investigations, correlate signals, reduce manual work, and help defenders respond at the speed modern threats demand. But as AI moves from experimentation into production, the next question becomes harder: can organizations operate it at scale without creating a new cost problem?

With code being written (& generated) faster than ever before, there is the unfortunate side effect that security vulnerabilities are also coming faster than ever before. Asking your LLM not to include security vulnerabilities in its code doesn't always work. It is becoming clear that the way software is built today, manually or with assistance, is insufficient when it comes to reliably, consistently, and provably writing secure code.

Most organizations spend their AI security budget on the wrong layer. The instinct is to just buy visibility to inventory the models, map the APIs, and ship a dashboard. But visibility alone won’t stop the coding agent that just pulled in a compromised MCP server. It won’t stop the production agent that’s about to forward a customer record to a place it shouldn’t go.

Walking the floor at Infosecurity Europe this week, it was impossible to avoid the subject of AI. Every conversation seemed to touch on it in some way. Vendors were demonstrating AI-powered detection capabilities, security teams were discussing governance frameworks, and practitioners were debating how best to secure the models, agents and data pipelines that are rapidly becoming part of everyday enterprise operations.

Bringing artificial intelligence into the office is a bit like adopting a hyper-energetic, brilliant, but chaotic intern. It can supercharge productivity, but if left unsupervised, it can accidentally delete the company database or invite a lawsuit. While the benefits of workplace AI are heavily advertised, deploying it without a safety net introduces significant vulnerabilities. Here’s a comprehensive breakdown of the risks businesses face when integrating AI into their daily operations.

NVIDIA NIM (NVIDIA Inference Microservices) packages production-ready AI models into optimized containers for enterprise deployment. Your developers need them. Your coding agents pull them. And until now, they pulled them directly from NVIDIA’s NGC registry, bypassing the supply chain controls you’ve spent years building. JFrog AI Catalog now brings NVIDIA NIM models under the same governance as every other artifact in your organization, with no separate registry and no governance gap.