Operationalizing Accuracy: The Control Layer Enterprises Need for Copilots and Agents

Accuracy is the control problem Gidi Cohen describes in his latest Substack article, and Bonfy’s value is giving enterprises the accuracy, context, and enforcement layer they need so AI, copilots, and agents can move fast without losing control of their data. This blog frames Bonfy as the way to operationalize that shift from “best‑effort detection” to trusted, in‑line control across human and AI workflows.

From noisy detection to trustworthy control

Gidi’s article makes a clear argument: legacy data security settled for noisy detection because humans could stay in the loop and enforcement was optional. In an AI‑driven world, where copilots and agents act inside workflows at machine speed, optional enforcement is the same as no enforcement at all.

For today’s enterprises, the real question is no longer “What can we detect?” but “What can we confidently enforce without breaking the business?” Bonfy was built around that question, with an entity‑aware platform that understands who data is about, who is using it, and in what context decisions are being made.

Why accuracy is different in the AI era

Traditional DLP and DSPM tools were designed for slower, human‑mediated workflows. They rely on patterns, static rules, and shallow classification that tolerate ambiguity because humans can review and correct later. In contrast, AI agents, copilots, and automated workflows continuously ingest, transform, and generate content across email, SaaS apps, collaboration tools, and custom LLM pipelines without natural pause points for manual review.

This shift makes three things non‑negotiable:

• Real‑time, in‑line evaluation of content and context, not just after‑the‑fact alerting.
• Entity awareness, as in knowing which customers, consumers, employees, or partners are implicated, referred, in a given piece of content.
• High‑precision enforcement that minimizes both false positives (business friction) and false negatives (actual exposure) so teams do not have to dial controls back to “monitor only.”

How Bonfy turns accuracy into control

Bonfy ACS (Adaptive Content Security) is designed specifically to bridge the detection–control gap Gidi highlights, using a single, multi‑channel platform that protects unstructured data everywhere it moves, such as email, files, SaaS apps, collaboration tools, AI systems, and AI agents.

Key elements that make enforcement viable:

• Contextual, entity‑aware analysis that ties content to specific customers, consumers, employees, systems, and AI agents, dramatically reducing false positives and enabling decisions that respect real business relationships and trust boundaries. In other words not just syntactical but also contextual.
• A unified policy and automation engine that orchestrates discovery, classification, labeling, remediation, and enforcement across channels, so organizations are not normalizing noise from fragmented tools.
• Automated, granular labeling, including publishing labels into Microsoft Purview, so downstream platforms can apply consistent controls without relying on brittle, manual tagging.

The result is a control layer that can safely block, modify, quarantine, or redirect risky actions without requiring a human in every loop, exactly the standard Gidi argues the industry must reach.

Extending accuracy into AI agents and MCP

In the article, the core tension is that AI collapses the gap between detection and action. Nowhere is that more visible than in agentic workflows, where agents plan tasks, call tools, access data, and trigger downstream actions autonomously.

Bonfy addresses the multiple leakage points in agent workflows (input prompts, data access, tool calls via MCP servers, and outbound channels) using the same intelligent platform across three layers of control.

• Input and data access: Bonfy controls grounding by applying contextual, entity‑aware labels and access policies to data sources such as SharePoint, Google Drive, email, and file stores, determining what content should even be available to a given agent.
• Output and downstream use: The platform inspects what agents ultimately send through email, collaboration tools, or files, preventing inadvertent disclosure of sensitive or customer‑specific data.
• Data‑in‑use via Bonfy’s MCP server/SDK/APIs: Bonfy exposes its own MCP server/other interfaces, so agents can call it during reasoning, asking “Is this content safe?” in real time and adjusting behavior based on risk, labels, or policy outcomes.

Because all three layers share the same “brain” (policies, knowledge graph, and entity‑aware analysis) customers get consistent decisions across classic channels and emerging agent frameworks, rather than stitching together separate tools.

What customers gain by closing the loop

For security and risk leaders, tying Gidi’s thesis to Bonfy’s platform translates into tangible outcomes:

• Safer AI adoption: Organizations can roll out copilots, SaaS assistants, and custom agents knowing there is a data‑centric control layer continuously watching what content they see, use, and generate.
• Reduction in operational noise: Entity‑aware accuracy cuts down false positives and manual triage, enabling smaller teams to manage larger AI surfaces without drowning in alerts.
• Governance that keeps up with AI: Unified, contextual visibility across humans, systems, and agents gives leaders the evidence they need to shape AI governance frameworks, satisfy regulators, and prove that AI‑related data risks are actually controlled, not just monitored.

Ultimately, Gidi’s point is that accuracy is not a feature; it is the foundation that turns observation into control. Bonfy exists to supply that foundation for the AI era, so enterprises can embrace agents and automation with confidence that their most sensitive information stays governed, wherever and however it moves.

If you'd like a demo of Bonfy ACS, click here.