Protecting AI agents means stopping sensitive data from reaching them — in both directions. Block .env files, API keys, and PII that employees paste into ChatGPT or Cursor, and redact PII that MCP-connected agents pull from Salesforce or Slack. Here's how to do it across browser, endpoint, and MCP.
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.env files, customer SSNs, credit card numbers, and source code into ChatGPT, Claude, and Cursor. Strac AI DLP's browser extension blocks or redacts that content before it ever leaves the browser.Most teams hear "protect AI agents" and picture prompt-injection defense or guardrails on what the agent says back. That matters, but it's not where regulated data leaks. The real exposure is the data that flows around the agent — and it moves in two directions.
Egress — what people send to the agent. Every day, employees paste and upload into ChatGPT, Claude, Cursor, and a dozen other AI tools. A developer drops a .env file in to debug a config error. A support rep pastes a customer's full record — name, email, SSN — to draft a reply. An engineer asks a coding agent to refactor a file that has an AWS access key hard-coded at the top. The moment that content hits the prompt box, it's headed for someone else's model, training pipeline, and retention window. This is the channel traditional DLP was built for, but most DLP tools don't sit in the browser where the paste happens.
Ingress — what the agent pulls in on its own. This is the newer and faster-growing exposure. AI agents no longer wait for a human to hand them data. Through MCP connectors, an agent reads directly from Salesforce, Slack, GitHub, Google Drive, and your cloud databases. A single tool call can return thousands of rows of PII, PHI, and card numbers — and none of it is inspected before it lands in the model's context window. The employee never copied anything. The agent reached in and took it.
Protecting AI agents means owning both directions: nothing sensitive goes out to an agent without a control, and nothing sensitive comes in through an agent without inspection. Do one and skip the other and you've left half the surface open.
The rest of this post walks through how Strac covers both — egress at the source (browser and endpoint) and ingress in the data path (MCP) — and the five control options you can apply to any finding.
The fastest leaks to an AI agent are the ones a human triggers: a paste, an upload, a drag-and-drop into a chat window. Strac AI DLP's browser extension sits exactly there — in the browser, watching every prompt before it reaches the model — and acts on sensitive content the instant it appears.
Here's what that looks like in the real situations security teams worry about:
.env file into ChatGPT to debug a broken environment variable. The file is wall-to-wall secrets — database URLs, API keys, OAuth client secrets. Strac detects the file as it's attached and blocks the upload before a single line leaves the browser, with a clear inline message telling the employee why.On the endpoint, Strac covers the desktop side of the same problem — native AI apps and file uploads from a managed device. Here the posture is monitor and alert: Strac sees when a sensitive file is moving toward a desktop AI client and raises a real-time alert with the data classes involved, so security has visibility into the desktop paths the browser extension doesn't see. Browser plus endpoint together close the egress surface across both how people reach AI tools.
Crucially, none of this is keyword blocklisting. Strac classifies the content — PII, PHI, PCI, secrets, source code, and content inside images and documents via OCR — so a .env file, a screenshot of a dashboard with a customer email in it, and a pasted SSN all get caught regardless of how they're labeled.
Blocking at the source handles what humans send. But the agent's own reach — ingress — needs a different control, because there's no paste to intercept. The data flows from a SaaS API straight into the model through an MCP tool call. Strac MCP DLP sits in that path.
The mechanics are straightforward. When an MCP-connected agent calls a read tool — say it pulls a Salesforce Opportunity or a Slack thread or a GitHub file — the raw response normally goes directly into the model's context window. Strac intercepts that response first, inspects the payload, redacts the SSNs, card numbers, emails, PHI, API keys, and source code it finds, and passes the clean payload to the agent. Non-sensitive, in-policy content flows through untouched.
A few examples of ingress in practice:
Files and attachments get the same depth of inspection: PDFs, DOCX, XLSX, ZIPs, and image attachments are parsed with OCR and document parsers, so sensitive content inside a scanned contract or a screenshot is found and redacted, not waved through. Every tool call is also logged — agent, user, tool, resource, data classes detected, redactions applied — which doubles as audit evidence for SOC 2, HIPAA, PCI, and GDPR.
This is the half of AI-agent protection most teams haven't covered yet. The full ingress story lives in the MCP DLP pillar; the broader AI-data-security picture is in AI DLP.
Detecting sensitive data is half the job. The other half is choosing what to do with each finding — and a one-size policy ("block everything") breaks workflows and trains people to route around the control. Strac gives you five actions, applied per data type, per channel, per policy:
.env file headed into ChatGPT, a private key in a prompt, a full card export. The content never moves, and the user sees why.[SSN REDACTED]). The rest of the prompt or tool response goes through. Best when the surrounding context is legitimately useful and only the regulated element needs to go.****-****-****-4242 — so the workflow that needs the last four digits still works without exposing the full number.The vault flow is what separates protection that people accept from protection they fight. Pure blocking creates shadow-AI workarounds; redaction-to-vault lets the agent finish the task and keeps the original retrievable, so security gets the control without becoming the bottleneck.
Policy is contextual: a customer SSN might be vaulted in a support workflow, masked in a finance dashboard, and outright blocked from leaving the browser into a public AI tool — same data type, different action, driven by your existing data classification rather than an AI-specific silo.
Protecting AI agents means stopping sensitive data from reaching them in both directions: egress (what employees send or upload to an AI tool — pastes, file uploads, commits) and ingress (what an agent pulls in on its own through MCP connectors to SaaS and cloud). Real protection covers both. Strac handles egress at the source with a browser extension and endpoint monitoring, and ingress in the data path with MCP DLP.
Use a browser-based AI DLP control that inspects every prompt before it reaches the model. Strac AI DLP's browser extension detects secrets, .env files, API keys, SSNs, and card numbers as they're pasted or uploaded and either blocks the action or redacts the sensitive element inline. It classifies content (not keywords), so a screenshot with a customer email or a .env file gets caught regardless of how it's labeled.
Egress is human-triggered — someone pastes or uploads sensitive data into an AI tool, and you intercept it at the browser or endpoint. Ingress is agent-triggered — an MCP-connected agent reads regulated data from Salesforce, Slack, or GitHub directly into its context window, with no human paste to catch. The two need different controls: source-side blocking and redaction for egress, data-path redaction (MCP DLP) for ingress. Cover only one and half your surface stays open.
Yes. Strac detects .env files, API keys, OAuth tokens, JWTs, SSH and private keys, and cloud access keys (AWS, GCP, Azure) — 48+ credential patterns — at the point of upload or paste in the browser, and on the endpoint as files move toward desktop AI apps. The default action for high-severity secrets is block, with redact and vault available where the workflow needs the surrounding content.
It doesn't have to. That's the point of the vault option. Instead of blocking everything, Strac can redact sensitive values into a vault — the agent operates on safe placeholders and completes its task, while the original value stays encrypted and retrievable for authorized users. You get the control without forcing people into shadow-AI workarounds.
PII (SSN, driver's license, passport, address, phone, email), PHI (clinical notes, MRN co-occurrence, ICD-10 codes near identifiers, lab values), PCI (full and partial card numbers via Luhn check), credentials and secrets (API keys, AWS/GCP/Azure access keys, OAuth tokens, JWTs, SSH and private keys), source code, and content inside files and images via OCR. Custom detectors map to your own data classifications.
No. Strac is agentless. The browser extension deploys to managed browsers, the MCP DLP layer drops into your AI client's MCP configuration with one endpoint change, and there are no application or agent SDK changes. First workspace is live in under 10 minutes.
Protection is the act stage of AI agent governance — the enforcement that follows discovery and monitoring. First you discover the AI agents operating in your environment, then you monitor what they're doing, then you protect by blocking and redacting sensitive data on the egress and ingress paths. All three live under the AI agent governance program.
AI agents are now both a destination and a source for your most sensitive data. Employees send secrets, .env files, and customer PII out to them through the browser. Agents pull PII, PHI, and card numbers in through MCP connectors to your SaaS and cloud. Protecting AI agents means owning both directions — blocking and redacting at the source, redacting in the data path — with the flexibility to block, redact, mask, tokenize, or vault each finding so work keeps moving.
Strac gives you that control plane across browser, endpoint, and MCP, deployed agentless in under 10 minutes, with every action logged as compliance evidence. Discover your agents, monitor their activity, and protect every path regulated data can take to and from an AI agent — without making each new AI tool a separate security exception.
If your team is using ChatGPT, Claude, Cursor, or MCP-connected agents on data that matters, book a 30-minute demo. We'll show you the browser blocking, the MCP redaction, and a policy plan for your specific AI tools.
For the broader program this sits inside, see AI agent governance, and its sibling stages: discover AI agents and monitor AI agents. For the ingress control plane across every SaaS surface, see MCP DLP and AI DLP.
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