Use Case: Data Security

AI-driven workflows and SaaS collaboration have shattered traditional data perimeters, pushing sensitive information through emails, file shares, SaaS apps, AI assistants, and autonomous agents at a speed legacy DLP and DSPM tools cannot see or control. Security teams lack unified visibility into which AI systems access, retain, index, or generate sensitive content, leaving organizations exposed to oversharing, shadow AI, data leaks, and compliance failures.

At the same time, existing tools rely on brittle pattern matching and static rules that generate high false positives, miss nuanced violations, and create so much operational friction that enforcement is often disabled or dialed back. This combination of blind spots, noise, and fragmented coverage makes it nearly impossible to enforce data security policies consistently across humans, systems, and AI.

Bonfy ACS delivers adaptive data security for the AI era by understanding both the “what” and the “who” behind every piece of content, across all major communication and collaboration channels. It analyzes human- and AI-generated content in real time, classifies and labels it with business and regulatory context, and enforces policy with precise, low-noise controls.

Instead of focusing only on files or one cloud, Bonfy provides a unified, multi-channel architecture that protects data in motion, at rest, and in use across platforms like Microsoft 365, Google Workspace, Salesforce, HubSpot, Slack, and AI tools such as Copilot and ChatGPT. Organizations gain continuous visibility, automated remediation, and audit-ready reporting that align data security with AI governance and compliance requirements.

Bonfy ACS uses a multi-layer, entity-aware content analysis engine that extracts sensitive entities, enriches them with business context, and evaluates them against rich policies in real time. A self-supervised Knowledge Graph learns your organizational structure, customers, consumers, roles, and relationships directly from business systems, allowing Bonfy to distinguish generic content from customer-specific or regulated information with high accuracy.

Entity-Aware Content Understanding

Bonfy ties every content event back to the people, customers, partners, systems, and AI agents involved, allowing policies to reflect real business relationships and trust boundaries instead of generic pattern-matching rules. This entity-aware approach reduces false positives and negatives, enabling precise enforcement that security teams can actually turn on without disrupting the business.

Unified, Multi-Channel Enforcement

A single policy and automation layer governs data in motion, at rest, and in use across email, SaaS apps, collaboration platforms, and AI workflows, eliminating channel-specific blind spots and fragmented rule sets. This unified enforcement model streamlines operations, simplifies audits, and ensures consistent protection as AI adoption and data movement patterns evolve.

Reduced risk of data leaks and oversharing across email, SaaS, collaboration tools, and AI systems with unified, contextual visibility.

Safe, scalable AI adoption by governing what data goes into LLMs and agents and what sensitive content can appear in AI-generated outputs.

Higher accuracy and lower noise than legacy DLP, enabling trusted, prevention-ready enforcement.

Lower total cost of ownership by consolidating point tools and reducing manual rule tuning and alert triage.

Stronger compliance and AI governance with consistent labeling, explainable findings, and audit-ready activity histories.

Advanced Data Security

The Challenge

The Solution

How Bonfy Solves the Problem

Assess Your Data Security Posture

Key Benefits of Bonfy ACS™

Bonfy Resources

Case Study

Day in the Life

Datasheet

Recent Bonfy Blogs

The Over‑Permissioning Time Bomb Inside Microsoft 365 Copilot

Understanding the OWASP Top 10 Risks for LLM & GenAI Apps, with a Focus on Data Security

Reducing Alert Fatigue: How Risk-Based Prioritization Lowers Security TCO