comparison
Oct 7, 2026A 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.
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.
| 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 |
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 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.
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.
.env, or only bulk file exfil?Use DLP governance for the hook + sensitive-session + gateway stack, and security & compliance for org design.
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.