Originally published on ReclaimLLM: https://reclaimllm.com/blogs/hook-dlp-vs-endpoint-ai-dlp-vs-aidr
A fair comparison for evaluation queries: tool-level hook redaction, OS/endpoint AI DLP agents, and network AIDR/CASB-style controls — what each sees and misses.
Hook DLP vs endpoint AI DLP vs AIDR
Buyers evaluating “AI DLP” often compare three architectures that sound similar in a slide deck and behave differently on a developer laptop.
This article maps hook DLP, endpoint AI DLP, and AIDR / network DLP without claiming one product is universally “best.” For a productized side-by-side, see also Hook DLP vs Endpoint DLP.
Definitions
| Approach | Where it sits | Typical strength |
|---|---|---|
| Hook DLP | Inside the coding assistant’s tool/file/shell hooks | Sees structured agent I/O; can redact before model egress |
| Endpoint AI DLP | OS agent / EDR-adjacent sensors on the device | Broad process visibility; heavier footprint; may miss app-native tool schemas |
| AIDR / network DLP / CASB | Proxy, gateway, or cloud API inspection | Strong for SaaS HTTP paths; weak for local CLI tool loops that never hit that proxy |
Hook DLP
Hooks run at the assistant boundary: file reads, shell results, and related tool payloads can be inspected and rewritten locally. ReclaimLLM uses this model for Cursor, Claude Code, and other supported assistants — replacing secrets with placeholders such as [REDACTED:API_KEY] before the request leaves the machine (sensitive data docs).
Fits well when: the risk is .env and terminal secrets in agent loops; you want low friction for developers; you need typed redaction that keeps sessions usable.
Limits: coverage depends on hook support per tool; browser ChatGPT threads and unmanaged apps need other paths (AI coding assistant DLP).
Endpoint AI DLP
Endpoint products instrument the OS: process trees, clipboard, file access, sometimes screen or keystroke-adjacent signals. They can catch exfiltration that never uses a coding-assistant hook.
Fits well when: you already standardize on an endpoint stack and need cross-application coverage beyond IDEs.
Limits: coding agents speak structured tool protocols; generic sensors may alert late, miss semantic context, or create noise. They rarely provide assistant-aware placeholders that preserve agent syntax. Fair evaluation should test secret-in-.env-via-agent, not only file copy to USB.
AIDR / network DLP / CASB
Network and API controls inspect traffic to known LLM SaaS endpoints. They work when traffic is forced through the inspection point and payloads are visible (not end-to-end opaque in a way that blocks inspection).
Fits well when: cloud ChatGPT / API usage is the dominant path and TLS interception or provider APIs are in scope.
Limits: local CLI agents, editor-embedded models, and bypass networks can avoid the chokepoint. Cursor/Claude Code tool loops often look like “developer workstation → provider” without a corporate proxy in the middle unless you force one.
Evaluation questions (fair tests)
- Does the control see agent file reads of
.env, or only bulk file exfil? - Does it redact before model ingest, or only alert after?
- What is the developer friction (agents broken vs placeholders)?
- Which tools in your inventory (Cursor, Claude, Copilot, Gemini, browser) are actually in path?
- Can you produce evidence for review — sessions, redaction config, gateway logs?
Use DLP governance for the hook + sensitive-session + gateway stack, and security & compliance for org design.
Practical recommendation
Most engineering orgs need layered controls: hook DLP for coding agents, endpoint for broader device risk, network/AIDR for sanctioned SaaS HTTP. Treat them as complementary. Evaluate with real Cursor/Claude workflows, not only generic web-chat proxies.
Originally published at https://reclaimllm.com/blogs/hook-dlp-vs-endpoint-ai-dlp-vs-aidr
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