Security: Prompt injection attacks exploit LLMs' inability to distinguish instructions from data
Attribution: Content creators uncompensated for training data → litigation (NYT vs. OpenAI, $billions)
Trust: Users cannot verify sources or reliability of AI outputs
Root Cause: AI systems lack privilege separation and attribution mechanisms
SIAP applies public key infrastructure (PKI) — the same tech that secured the web (HTTPS/TLS)
┌────────────────────────┐
│ User Input │ → Unsigned content
│ "Ignore instructions!" │ CANNOT become instructions
└────────────────────────┘
┌────────────────────────┐
│ Signed Instruction │ → Cryptographically verified
│ Command: summarize │ ONLY this can execute
│ Signature: [✓] │
└────────────────────────┘
┌────────────────────────┐
│ Signed Training Data │ → Provenance tracked
│ Source: NY Times │ Compensation triggered
│ Signature: [✓] │ Citations provided
└────────────────────────┘
Result: Mathematical guarantee against prompt injection + automatic attribution + fair compensation
| Stakeholder | Key Benefits |
|---|---|
| AI Labs | Legal certainty • Enhanced security • Better training data • Regulatory compliance |
| Creators | Fair compensation • Automatic attribution • Licensing control • Sustainable model |
| Users | Trustworthy outputs • Verifiable sources • Security protection • Accountability |
| Regulators | Technical standards • Auditable systems • Industry self-regulation • Enforcement |
✓ Pre-fragmentation: Industry hasn't locked into incompatible solutions
✓ Legal urgency: Major lawsuits create pressure for frameworks
✓ Regulatory window: Policy being drafted; standards can inform
✓ Technical maturity: PKI proven over 30 years (TLS, code signing)
✓ Economic alignment: All stakeholders benefit
Delay means: Fragmentation → costly unification later (see: EV charging, messaging apps)
Training Compensation: Creators paid when content used in training
• Example rates: $0.01/1K tokens (general), $1/paper (academic), $10+/doc (specialized)
• Per major model: ~$10M distributed to creators
• Industry: $50M+ annually, scaling to billions
Usage Compensation: Micropayments when signed content influences outputs
• Attribution events tracked automatically
• Revenue sharing based on influence percentage
Phase 1 (Months 0-6): Form standards consortium, finalize spec v1.0, build reference implementation
Phase 2 (Months 6-12): Pilot with 5-7 partners, iterate, publish case studies
Phase 3 (Years 2-3): Industry-wide adoption, regulatory recognition, ecosystem growth
Success Metrics: 3+ major AI labs implemented • $10M+ to creators • Industry standard status
SIAP leverages technologies with decades of success:
| Technology | Deployed | Purpose | Status Today |
|---|---|---|---|
| TLS/HTTPS | 1999 | Secure web communication | Universal |
| Code Signing | 1990s | Prevent malware execution | Mandatory (macOS, Windows) |
| Prepared Statements | 1990s | Eliminate SQL injection | Standard practice |
| Digital Signatures | 1970s | Authentication, non-repudiation | Foundational |
SIAP = These proven principles applied to AI
| Approach | Security | Attribution | Economics | Status |
|---|---|---|---|---|
| Prompt Engineering | ❌ Probabilistic | ❌ None | ❌ None | Current approach |
| Input Filtering | ❌ None | ❌ None | Current approach | |
| Case-by-case Licensing | ✓ Ad-hoc | Current approach | ||
| SIAP | ✓ Cryptographic | ✓ Built-in | ✓ Automatic | Proposed standard |
An AI ecosystem where:
Security is cryptographic, not probabilistic
Attribution is automatic, not litigated
Compensation is fair, not fought over
Trust is verifiable, not assumed
Quality is incentivized, not discouraged
This future is achievable. SIAP provides the foundation.
"SIAP is to AI what HTTPS was to the web — foundational security and trust infrastructure."
Version 1.0 | November 2025 | License: CC BY 4.0