Iboss Unveils AI DLP for Real-Time Data Controls Across Cloud and AI

iboss announced AI DLP on November 3, 2026, a data-loss prevention product designed to inspect content in real time across AI tools, cloud applications and other online destinations. The company plans to open a private preview in December 2026.

The product targets limitations in conventional data-loss prevention. Rules based on regular expressions and pattern matching look for formats defined in advance, such as an account number or ID string, while label-dependent controls can miss files that were labeled incorrectly or never labeled. Examples include a file shared to a cloud drive, a form submitted to a web application and source code pasted into an AI tool, all of which can carry sensitive material without fitting a fixed pattern.

Content Inspection and Policy Controls

AI DLP combines AI and machine-learning classification with deep content inspection to examine data as it moves. File analysis and optical character recognition extend that inspection to documents and text inside images, covering content that is labeled, mislabeled or unlabeled. The design bases enforcement on the substance of a file or prompt, including when format rules and metadata provide no reliable signal.

Policies can be scoped by user, group or department to identify, flag or stop transfers involving source code, customer data, personally identifiable information and intellectual property. The iboss DLP documentation also describes source and destination scoping, named exceptions, and responses including blocking, file capture, logging, alerts and severity assignment. Violations can roll into risk-scored incidents with event timelines and captured-file evidence, giving analysts a case to investigate and close instead of only individual log entries.

Encrypted Traffic and Agent Activity

Content-aware controls require visibility into more than a destination. iboss’s explanation of AI DLP says that web-based AI tools generally require encrypted-session inspection for this level of control; without it, a system may see where data is going but not the prompt or file being sent. That visibility allows content policies to apply to AI chats in the same way as other web uploads.

The controls also extend beyond a person pasting text into a chat window. They can govern which AI services, tools and APIs an autonomous agent may use and retain a record of agent activity. This connects agent governance to the same enforcement model used for interactive AI sessions, covering data movement initiated by software as well as by employees.

Labels, Audit Evidence and Platform Architecture

Where Microsoft Information Protection sensitivity labels exist, AI DLP reads and enforces them inline. The product documentation describes per-label, per-destination allow or block decisions using Microsoft Purview label synchronization, while content inspection continues to cover unlabeled documents. Labels therefore become one policy input alongside content analysis, reducing dependence on every document carrying the correct metadata.

Reporting includes data-loss risk scoring and one-click reports for GDPR, HIPAA, PCI-DSS and SOX, intended to give auditors evidence of what was found and stopped. iboss says each customer’s traffic is processed in dedicated containers with isolated SSL keys and dedicated IP addresses, and that customer data is not co-mingled. AI DLP sits alongside the company’s Secure Web Gateway, CASB, GenAI Protection and Zero Trust Network Access services, with a shared console, policy engine and data lake.

A 30-minute online demonstration is scheduled for November 5, 2026, at 12:00 p.m. ET, ahead of the December preview. The practical distinction is between announced design and availability: iboss has laid out a single content-aware control plane for human and agent-driven data movement, and the private preview is the scheduled step toward putting that design into practice.