Security, IP, and Compliance
AI tools introduce new risk vectors. Understanding and managing them is essential for responsible adoption.
Data flow risks
Section titled “Data flow risks”What data goes where?
Section titled “What data goes where?”When developers use AI tools, code and context flow to external services:
Prompts include:
- Code snippets (sometimes entire files)
- Error messages and stack traces
- File names and structure
- Comments (which may contain sensitive info)
Know your data flow:
- What leaves your network?
- Where is it processed?
- Is it stored? For how long?
- Is it used for model training?
Mitigation strategies
Section titled “Mitigation strategies”Enterprise agreements: Most vendors offer enterprise plans with data handling guarantees. Review them carefully.
Data classification: Define what can/cannot be shared with external AI services.
Local models: For highly sensitive work, consider local or self-hosted models.
Code filtering: Some tools allow excluding sensitive paths/patterns.
Intellectual property considerations
Section titled “Intellectual property considerations”The training data question
Section titled “The training data question”AI models were trained on public code. This raises questions:
License contamination: Could AI-generated code introduce license obligations?
Copyright status: Who owns AI-generated code?
Patent implications: Could AI output infringe patents?
Current legal status: Uncertain and evolving. Courts are still deciding.
Practical approach
Section titled “Practical approach”Document AI usage: Know where AI was involved in your codebase.
Review for obvious copying: Reject output that looks like verbatim reproduction.
Legal consultation: For high-stakes situations, involve legal counsel.
Watch the legal landscape: Precedents are being set now.
Your proprietary code
Section titled “Your proprietary code”The concern: Code you share with AI tools could influence model training, potentially benefiting competitors.
Mitigations:
- Enterprise agreements with training opt-out
- Self-hosted models for sensitive code
- Limiting what context is shared
Compliance requirements
Section titled “Compliance requirements”Regulated industries
Section titled “Regulated industries”Healthcare, finance, government, and other regulated sectors have specific requirements:
Data residency: Where can code/data be processed?
Audit trails: Can you demonstrate what AI was used for?
Access controls: Who can use AI tools? On what data?
Incident response: What if AI exposes sensitive data?
Compliance checklist
Section titled “Compliance checklist”- Data handling agreements reviewed with legal
- Data residency requirements checked
- Audit logging in place for AI tool usage
- Access controls appropriate for data sensitivity
- Incident response plan updated for AI scenarios
- Employee training on AI data handling
Security of AI-generated code
Section titled “Security of AI-generated code”New attack vectors
Section titled “New attack vectors”AI-generated code can introduce vulnerabilities:
Insecure patterns: Models may generate code with known vulnerabilities.
Dependency confusion: Agents may suggest packages that don’t exist (or malicious ones that do).
Logic vulnerabilities: Subtle security bugs in plausible-looking code.
Mitigation
Section titled “Mitigation”Security scanning: Run SAST/DAST on all code, regardless of origin.
Dependency verification: Verify all packages suggested by AI exist and are legitimate.
Human review for security-sensitive code: Don’t let AI generate auth, encryption, or input validation unreviewed.
Penetration testing: Include AI-generated code in security assessments.
Governance structures
Section titled “Governance structures”Policy requirements
Section titled “Policy requirements”At minimum, define:
Usage policy: Who can use what tools, on what code.
Data handling policy: What can be shared with external AI.
Review requirements: How is AI code reviewed and approved.
Incident handling: What to do if AI causes a security issue.
Accountability
Section titled “Accountability”Clear ownership: Humans own the code they commit, regardless of AI involvement.
Audit capability: Be able to identify AI involvement in code if needed.
No blame-shifting: “The AI did it” is not an excuse.
Resources
Section titled “Resources”Essential
Section titled “Essential”- Government Agents – Mark Myshatyn, Los Alamos - AI agents in high-security regulated environments