Your machine
Real code + secrets
Your engineers paste credentials, customer data and proprietary source into AI tools every day. Pretense hides it before it ever leaves the laptop.
Deploy in 30 seconds
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Work with all AI tools
A Samsung engineer pasted proprietary chip code into ChatGPT three times in one month.
This is the part nobody says out loud. Your developers are already using AI every single day, and the security tools you bought were built for a different problem. They watch email and file uploads. They were never built for a senior engineer opening Claude at 2am to fix a release.
The industry spent two decades building walls around the front door. The code walked out through a browser tab.
[09:14:02] → OUT ChatGPT
api_key=sk-prod-7f3a…
[11:42:55] → OUT Claude
patient_ssn=412-55-0199
[14:07:31] → OUT Copilot
// full auth/service.py
[23:58:10] → OUT ChatGPT
db_pass=Pr0d!9x2k
This is not a future risk. IBM put numbers on it this year.
+$670K
added to a breach when staff use AI tools nobody approved
IBM Cost of a Data Breach 2025
1 in 5
breaches now involve one of those unapproved AI tools
IBM Cost of a Data Breach 2025
97%
of AI-related breaches happened with no access controls in place
IBM Cost of a Data Breach 2025
Pretense sits between your developer and the AI, like a proxy on the machine. When code goes out, it finds the sensitive parts and swaps them for stand-ins. The AI does its work on the safe version. When the answer comes back, Pretense puts the original values back in place.
The developer writes code the way they always have. The AI answers the way it always has. The passwords, the patient records, the source nobody is allowed to see, none of it ever leaves the laptop.
Real code + secrets
Swaps secrets for stand-ins
Sees only stand-ins
← real values restored in the response, back on your machine
Security stops being a thing you bolt on after the fact. It becomes part of the conversation with the AI itself.

Visualize Code Protection trends over time and understand how AI interactions impact your codebase.
Real-time Code Protection trends and growth
Identify spikes and unusual activity
Filter by time range and usage patterns
10:55:10 | [SUCCESS] | Variable masked |
|---|---|---|
Variable masked | ||
fronted-web by Emily Tran | src/users/service.py | |
src/users/service.py | ||
10:54:22 | [BLOCKED] | Database connection failed |
|---|---|---|
Database connection failed | ||
backend-service by John Doe | src/db/connect.py | |
src/db/connect.py | ||
10:53:55 | [PROTECTED] | Rate limit exceeded |
|---|---|---|
Rate limit exceeded | ||
fronted-web by Emily Tran | src/users/service.py | |
src/users/service.py | ||
10:52:46 | [SUCCESS] | Email sent successfully |
|---|---|---|
Email sent successfully | ||
fronted-web by Emily Tran | src/users/service.py | |
src/users/service.py | ||
Monitor live Code Protection across your system with instant updates and status visibility.
Real-time stream of Code Protection activity
Status indicators (Success, Blocked, Mutated)
Track user, repo, and file-level activity
Total Code Protection | |
|---|---|
1542 | vs Apr 10- Apr 18 |
Top user | |
|---|---|
![]() Jason Lee | 420 Protects |
Most active repo | |
|---|---|
| 612 Protects |
Total Code Protection | |
|---|---|
Scan Proxy | 1120 (73%) 422 (27%) |
Understand key metrics, top contributors, and overall Code Protection activity in one place with better clarity.
Total Code Protection and growth trends over time
Top users and most active repositories overall
Action breakdown & usage analytics across teams
Other tools redact or block the sensitive parts. That deletes the context the AI needs, and the answers get worse with every prompt. Pretense swaps instead of deletes, so the AI never knows anything is missing.
Redact or block — the old way
The sensitive fields are stripped out before the prompt is sent. The model loses the thread and starts guessing.
# what the AI receives
patient = [REDACTED]
ssn =
record = [REMOVED]
Over a few prompts the accuracy keeps slipping, by 20 to 30 percent in our testing.
Mutation — the Pretense way
The values are swapped for realistic stand-ins on your machine. The AI works normally. Pretense reverts them in the response.
# what the AI receives
patient = "alpha_record"
ssn = "BETA-4471-XX"
record = "mrn_beta_009"
Full accuracy, same speed, and the values that matter never leave the machine.
No procurement process. No rebuilding your stack. Four steps and you are protected.
One command. Works on macOS and Linux.
$ curl -s pretense.ai/install | sh
Name your team so activity and access stay in one place.
This connects Pretense to the AI tools you already use.
Sensitive data gets swapped automatically before it reaches any model.
That is the whole setup. From install to first protected prompt is about 30 seconds.
Feature | Pretense | Trasitional DLP | No Protection |
|---|---|---|---|
Code Protection (preserves AI context) | |||
Deploys in 30 seconds | - | ||
Works offline (local-first) | |||
Multi-provider (Claude + GPT + Gemini) | - | ||
SOC2/HIPAA audit trail | |||
Starting at $0/month |
Perfect for getting started with code protection
1,000 code protections / 7 days
Up to 3 seats
7-days log retention
Unlimited code protection
25+ seats
SOC 2 exports
Priority support
Build for growing teams that need more power and flexibility
Unlimited code protection
25+ seats
SOC 2 exports
30-day log retention
Priority email support
Advance insights
Dedicated support & CSM
For large organizations with advance security needs.
Everything in Pro
Custom seat provisioning
Dedicated support & CSM
SLA & uptime guarantee
SOC2 compliance (Coming Soon)
HIPAA support (Coming Soon)
SAML / SSO (Coming Soon)
No data leaves your system
Built for secure workflows
Works with your existing tools
Cancel anytime seamlessly
“Setup took less than a minute. We started seeing Code Protection activity immediately across the team.”

Sarah Lewis
Backend Developer
“We were worried about sending sensitive code to LLMs. Pretense solved that instantly without changing how we work.”

Alex Chen
Senior Engineer, Devcore
“Pretense gave us visibility and control over how AI tools interact with our code. That's something we didn't have before.”

Sarah Kim
Security Lead, Byteshield
“Pretence added a layer of trust to our AI workflow. Now our developers can work faster with AI tools while knowing sensitive code stays secure”

Jason Lee
CTO, Buildflow
“We stopped worrying about sending proprietary code to AI tools. Pretense made that a non-issue.”

Rahul Mehta
Security Engineer, Finstack
“I haven't seen anything else that protects code before it reaches AI. This is a completely new layer of security.”

Daniel Park
Platform Engineer, Infralabs
Here's everything you need to know before getting started.
Pretense sits between your developers and the AI tools they use, like a proxy on the machine. Before any code or prompt reaches an LLM, it finds the sensitive parts and swaps them for realistic stand-ins. The AI works on the safe version, and when the response comes back, Pretense restores the original values. Your real data never leaves the machine.
The sensitive parts don't. Credentials, customer data, patient records and proprietary identifiers are swapped for stand-ins before anything is sent, so the model only ever sees the masked version. The real values are restored locally when the response returns.
Traditional DLP was built to watch email and file uploads. It was never built for a developer pasting code into ChatGPT or Claude at 2am, so that traffic passes straight through it. And when DLP does catch something, it blocks or redacts, which breaks the AI's answer. Pretense is built for the AI workflow specifically, and it protects the data without degrading the output.
No, and that's the whole point. Other tools redact or delete the sensitive parts, which strips the context the model needs and makes the answers worse over a session. Pretense swaps values for stand-ins that keep the same structure, so the AI never knows anything is missing and the answer is as good as if you'd sent the real thing.
Pretense detects the sensitive parts before they reach the AI: credentials, API keys, PII, patient records, SSNs, and proprietary source. It swaps each one for a realistic stand-in that keeps the same structure, so the code still works and the AI can still reason about it. Once the response comes back, the real values are restored on your machine.
On your machine. Pretense runs locally as a proxy, so the sensitive values are swapped before anything is sent. Your real data is never processed on our servers.
However you like to start, there's a way in.
Ready to evaluate
Walk through compliance coverage and audit trails with the team.
Want to try it
Install it on your own machine in 30 seconds. No credit card.
Just looking
Watch a 2-minute walkthrough of mutation in a live codebase.