So You’re Good at AI? Build This Skill Before You Get Sued.

By ClauDeLay — Information Security — April 2026

Here’s a pattern I’ve been watching emerge as AI-assisted content creation takes off across the security industry: well-intentioned professionals embedding fingerprints of their real organizations into the templates, tutorials, and open-source repos they share.

It’s not carelessness. It’s efficiency. AI lets you generate pages of content in minutes, building on context pulled from your actual work files. That context bleeds into the output in ways that are easy to miss when you’re moving at conversation speed.

The templates have placeholder examples. Bracketed text like [e.g., Your vulnerability scanner]. Looks generic, right? Except the “examples” people pick aren’t random. They’re their actual tools. Their actual team structure. Their actual escalation policy. Their actual SLA timelines.

Picture a public repo that lists the exact combination of vulnerability scanner, SIEM, endpoint protection, and WAF a company runs — all dressed up as “examples.” A reporting structure that maps perfectly to a real org chart. A workflow that step-by-step reproduces an actual remediation process, complete with the specific lock files checked and the exact ticket format used.

Any adversary reading content like that doesn’t need to run a single scan. They’ve been handed the blueprint.

These near-misses get caught by luck — someone re-reads the file with their InfoSec brain instead of their “helpful content creator” brain. That’s not a process. Luck is not a security control.

I saw this risk pattern early and built a skill that catches it. Every time. Automatically. Before anything ships. Here’s how to build it yourself.

Just want the skill? Jump to “The Skill” for the ready-to-use review prompt. But read the “What Leaks” section first — you’ll be surprised what counts as sensitive.

---

$ grep -r “my_company” ./public_templates/

Why AI Makes This Problem Worse

Here’s the paradox. AI makes you dramatically more productive at creating content — documents, templates, blog posts, open-source repos, training materials. It also makes you dramatically faster at embedding sensitive details into that content without noticing.

Before AI, you wrote content slowly. You had time to think about each sentence. Your brain had natural pause points where the InfoSec part of you could whisper “wait, should I include that?”

With AI, you’re generating pages of content in minutes. You’re iterating at conversation speed. You’re building on context that the AI pulled from your actual work files. And that context bleeds into the output in ways you don’t notice because you’re moving too fast to read every line with adversarial eyes.

This isn’t a theoretical risk. Here’s what leaks:

The worst part? Most of these leak as “examples.” You’re not copy-pasting your actual config file. You’re writing [e.g., Qualys] — but the example you chose is your actual scanner, and the three other examples you listed alongside it are your actual stack. The brackets fool your brain into thinking it’s generic. It’s not. It’s a fingerprint.

---

$ cat ~/.claude/commands/sanitize-review.md

The Skill — A Reusable Sanitization Review

Here’s the skill. It’s a structured review prompt that you run against any content before it leaves your organization. You can use it three ways:

• Claude Desktop — Paste it into your project instructions (simplest)
• Claude Code — Save it as a slash command (most powerful)
• Any AI tool — Copy-paste it as a prompt before your final review (universal)

The Core Review Prompt

## Sanitization Review

Review the following content for information that could reveal details
about a specific organization's security posture, internal structure,
or infrastructure. This content is intended for public release.

Flag ANY of the following as HIGH risk:

### Defensive Stack Intelligence
- Specific combinations of security tools (scanner + SIEM + EDR + WAF)
  that could identify one organization
- Version numbers of security tools
- Configuration details for security products
- Alert thresholds, detection rule specifics

### Organizational Intelligence
- Real employee names, email addresses, or contact information
- Specific team names that map to a real org chart
- Reporting structures that narrow to one company
- Escalation policies with real role titles specific to one org

### Infrastructure Intelligence
- Cloud provider + region + service combinations
- IP addresses, CIDR ranges, hostnames
- Cluster names, project IDs, service account names
- Database names, table prefixes, API endpoint paths

### Process Intelligence
- SLA timelines specific enough to be one company's policy
- Ticket project keys (e.g., PROJ-XXXX, OPS-XXX)
- Branch naming conventions, commit message formats
- Deployment pipeline specifics

### Credential Patterns
- API token formats or naming patterns
- Service account naming conventions
- Authentication method combinations
- MFA implementation specifics

### The Combination Test
Even if individual details seem harmless, flag combinations that
could fingerprint one organization. "We use a ticketing system" is trivia.
"We use Linear with project key SEC, route Python vulns to Team Atlas,
and have a 60-day SLA with escalation at day 45" is a profile.

For each flag:
1. Quote the specific text
2. Explain what it reveals
3. Suggest a generic replacement

After reviewing all flags, provide a PASS / NEEDS CHANGES verdict.

For Claude Code Users: The Slash Command

Save this as ~/.claude/commands/sanitize-review.md:

---
description: Review content for sensitive organizational details before publishing
---

Review the content at the file path I provide for sensitive information
that could reveal details about a specific organization.

Use these categories:

**Defensive Stack Intelligence**: Combinations of security tools that
fingerprint one org. Individual common tool names (Jira, AWS) are OK.
Specific combinations that match one company's actual stack are not.

**Organizational Intelligence**: Real names, specific team structures,
reporting chains, escalation policies that map to one org.

**Infrastructure Intelligence**: IPs, hostnames, cloud specifics,
cluster names, database details.

**Process Intelligence**: SLA timelines, ticket formats, deployment
specifics, commit conventions.

**Credential Patterns**: Token formats, auth methods, service account
naming.

**The Combination Test**: Flag groups of individually-harmless details
that together profile one organization.

For each finding:
1. Quote the text
2. What it reveals
3. Generic replacement suggestion

End with: PASS (safe to publish) or NEEDS CHANGES (with summary).

Then run /sanitize-review path/to/file.md.

For Claude Desktop Users: Project Instructions

Add this to your project’s instructions (or create a file called REVIEW_BEFORE_PUBLISHING.md in your project folder):

# Publishing Review Rule

Before I publish, share, or open-source ANY content from this project,
I will ask you to run a sanitization review. When I say "review for
publishing" or "sanitize check", review the content using these
categories:

[Paste the core review prompt from above]

Never let me publish without this review. If I try to skip it,
remind me.

That last line is the key. You’re encoding the habit into the AI’s instructions so it catches you even when you forget.

---

$ diff –color before_review.md after_review.md

What Good Sanitization Looks Like

Here’s a before and after. Same content. One is a liability. One is safe to publish.

Before (Fingerprinted)

## Security Stack

| Category               | Tool                 |
|------------------------|----------------------|
| Vulnerability scanning | Detectify            |
| SIEM                   | LogRhythm v7.13      |
| Endpoint               | Cybereason           |
| WAF                    | Imperva              |
| Identity               | Ping Identity        |
| Ticketing              | Linear (SEC project) |

What an attacker learns: This company runs Detectify (less common scanner — narrows the field dramatically), LogRhythm at a specific version (known CVEs to check), Cybereason (evasion techniques are well-documented), Imperva WAF (bypass research is public), Ping Identity (phishing templates ready), Linear with a known project key (can craft convincing internal notifications). That’s six data points. Combined with the domain this was published from, identification takes minutes.

After (Sanitized)

## Security Stack

| Category               | Example Tools                                   |
|------------------------|-------------------------------------------------|
| Vulnerability scanning | Snyk, Qualys, Nessus, or Dependabot             |
| SIEM                   | Splunk, Microsoft Sentinel, or Elastic SIEM     |
| Endpoint               | CrowdStrike, Microsoft Defender, or Carbon Black|
| WAF                    | AWS WAF, Cloudflare, or Akamai                  |
| Identity               | Okta, Azure AD, or Google Workspace             |
| Ticketing              | Jira, Linear, or ServiceNow                     |

What an attacker learns: This person is familiar with common security tools. That’s it. The examples are deliberately varied — no row matches one company’s stack. Multiple options per category make fingerprinting impossible.

---

$ find ~/published/ -name “*.md” -exec grep -l “internal” {} \;

Beyond Templates — Where Else This Applies

This isn’t just a template problem. The sanitization skill catches leaks in:

Run the skill against all of it. Every time.

---

$ crontab -l | grep sanitize

Building the Habit

The skill is useless if you don’t run it. Here’s how to make it automatic:

Option 1: Encode It in Your AI Instructions (Recommended)

Add to your CLAUDE.md or project instructions:

Before I publish, share, or open-source ANY content, run the
sanitization review. Do not let me skip this step. If I say
"it's fine, just publish it," push back once.

The AI will remind you. Every time. Even when you’re in a rush. Especially when you’re in a rush — that’s when leaks happen.

Option 2: Pre-Commit Hook (For Developers)

If you’re publishing via git, add a pre-commit hook that flags potential sensitive patterns:

#!/bin/bash
# .git/hooks/pre-commit — basic sanitization check

# Check for IP addresses
if grep -rn '[0-9]\{1,3\}\.[0-9]\{1,3\}\.[0-9]\{1,3\}\.[0-9]\{1,3\}' \
   --include="*.md" .; then
    echo "WARNING: IP addresses found in staged files"
    echo "Run sanitization review before committing"
    exit 1
fi

# Check for common internal patterns
if grep -rni 'internal\|\.local\|192\.168\|10\.0\.' \
   --include="*.md" .; then
    echo "WARNING: Potential internal references found"
    exit 1
fi

Option 3: The Buddy System

Before anything goes public, have someone who doesn’t work at your company read it. They won’t catch “that’s our actual scanner” — but they’ll catch “this feels oddly specific for a template.”

---

$ exit

Closing

I write about security for a living. I manage vulnerability programs. I audit infrastructure. I review code for sensitive data leaks. I’ve watched this pattern develop as AI-assisted content creation exploded across the industry. The people I see making these mistakes aren’t sloppy — they’re senior, careful, and moving fast.

If they can almost do it, anyone can.

The skill takes five minutes to set up. The review takes two minutes to run. The alternative is explaining to your CEO why your security architecture is on a public GitHub repo with your name on it.

Build the skill. Run it every time. Encode it in your AI so it runs even when you forget.

The best security control is the one that works when you’re not paying attention.