Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Brand impersonation detection is the process of identifying fake domains, cloned brand experiences, and exposure signals that show attackers are using a trusted brand to deceive customers, employees, or partners. For security teams, the harder problem is not finding every impersonation asset. It is knowing which signals indicate live user exposure and which ones should change the response.

We’re excited to announce Workflow for KeeperPAM — a new capability that eliminates standing privilege by ensuring every access request is explicitly made, approved and time-bound. This capability ensures that access to PAM resources is time-bound, eliminating standing privilege, mitigating unnecessary risk and simplifying least-privilege compliance.

For years, Managed Service Providers (MSPs) built their businesses around infrastructure management, endpoint support and network reliability. But cyber threats have evolved significantly, and with them, the role of the modern MSP. Today’s cyber threats rarely begin with sophisticated malware or brute-force attacks against firewalls. Instead, cybercriminals target the easiest and most effective entry point into any organization: identities.

Regulators and auditors now demand immutable proof of every privileged action, attributed to an identity, across Secure Shell (SSH), Kubernetes, databases, and Remote Desktop Protocol (RDP).

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.

Long gone are the days where usernames were all you needed to secure a network. The same is true for your Security Operations Center (SOC) analysts trying to investigate a threat. "Who is jdoe05 and why are they logging into this server?" is a critical question to answer during an investigation, one that neither NDR (Network Detection and Response) nor EDR (Endpoint Detection and Response) can answer directly. Enter the Identity Provider (IdP).

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?