Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Software risk has always existed. What’s changed is the scale, speed, and economics of it. For decades, organizations operated under a relatively stable set of assumptions: humans write code, security teams scan it, vulnerabilities get prioritized and patched. The process was slow, imperfect, and often underfunded — but it was manageable. AI has dismantled those assumptions. And if your security program is still calibrated to the old model, you’re already behind.

As organizations accelerate adoption of AI systems, the scope of data security has dramatically expanded. Sensitive data is no longer simply stored. It is continuously accessed, transformed, and moved across cloud services, APIs, and AI pipelines. For use cases from model training to inference, AI systems depend on dynamic data flows that introduce new and often unseen risks.

Author: Alexander Ivanyuk, Senior Director, Technology Generative AI changes how people work with information. A user can ask a question, upload a document, summarize a ticket, draft an email or ask an AI assistant to help with a workflow. That is useful because the interaction feels natural. But the same natural-language interface also creates a new security problem: instructions and data can become mixed together.

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.