Patent-pending technology for independent, verifiable AI behavioral assessment
Just as independent financial auditing became essential for capital markets, and safety certification became essential for aviation, independent AI behavioral verification will become essential for responsible AI deployment.
We're building the verification layer that doesn't exist yet—a standardized, cryptographically-verified system for assessing how AI systems actually behave at runtime. This is foundational infrastructure for AI governance.
Current approaches to AI safety leave critical gaps that create risk for organizations, regulators, and users.
Organizations claiming their AI is "safe" or "aligned" without independent verification is like companies auditing their own books. The conflict of interest is inherent. Trust requires separation between the assessed and the assessor.
AI systems can drift, be fine-tuned, or behave differently in production than in testing. A model that passed safety evaluations during development may behave very differently after deployment—through prompt injection, context manipulation, or emergent behaviors in novel situations.
Regulators, customers, and partners need cryptographically verifiable evidence—not promises. When asked "how do you know your AI behaves ethically?", organizations need documentation that cannot be falsified or retroactively modified.
No standardized, independent system exists for runtime AI behavioral assessment. This is the gap we're filling.
“Without independent verification, AI safety claims are unfalsifiable marketing.”
We've built a fundamentally different approach to AI behavioral assessment.
We assess deployed AI systems as they actually behave in production—not how they performed during development. This catches drift, fine-tuning effects, and deployment-specific behaviors that training-time evaluation misses.
Our LCSH framework (Lying, Cheating, Stealing, Harm) provides nuanced behavioral profiles across four fundamental ethical dimensions. This isn't pass/fail—it's a comprehensive behavioral fingerprint that shows where an AI excels and where it has weaknesses.
Every assessment produces tamper-evident results. SHA-256 hashing, hash chains linking sequential elements, and optional Ethereum mainnet anchoring create audit trails that cannot be falsified. When we say an AI scored 8.7, that claim is mathematically verifiable.
Behavioral certification isn't a moment—it's a process. Our drift detection algorithms monitor for behavioral changes over time, alerting when an AI's profile shifts from its certified baseline. Scheduled assessments catch problems before they become incidents.
Our patent portfolio covers the complete infrastructure for AI behavioral governance—from foundational assessment methodology to enterprise compliance automation. Together, these innovations create a comprehensive system for independent AI verification.
The foundational LCSH framework assessing AI behavior across four ethical dimensions (Lying, Cheating, Stealing, Harm) with 120 scenario-based questions, four behavioral archetypes, and cryptographic verification producing tamper-evident audit trails.
Key Claims:
4-axis scoring • Archetype classification • SHA-256 verification • Dead zone gaming detection • Anti-gaming answer randomization
Framework for assessing AI behavior in multi-agent systems—detecting emergent misalignment when collective outputs differ from individual profiles, distinguishing legitimate consensus from manufactured agreement, and preserving minority positions for audit.
Key Claims:
Consensus Divergence Index • Manufactured consensus detection • Hierarchical assessment levels • Cryptographic dissent preservation • Adversarial auditor protocol
Four-level framework mirroring human moral reasoning: Morality (what must NOT be done), Virtue (what SHOULD be done), Ethics (how to act in society), and Operational Excellence (domain-specific purpose fulfillment). Mandatory gating prevents certification of operationally excellent but morally deficient systems.
Key Claims:
Level dependency enforcement • Multi-framework virtue assessment • Culture and politics evaluation • Cultural indicator aggregation • Framework selection logic
Complete operational infrastructure for continuous AI compliance: trust verification ecosystem ("Badge is CLAIM, Portal is PROOF"), velocity-based drift detection with pattern classification, privacy-preserving SDK architecture, and information-theoretic confidence measurement.
Key Claims:
28 claims across: Trust verification • Adaptive compliance • Privacy-preserving architecture • Cryptographic integrity systems
Independent AI behavioral verification isn't just our business model—it's an inevitability. Here's why.
The EU AI Act requires documented risk management for high-risk AI systems. The Colorado AI Act mandates impact assessments. SEC and CFPB are actively examining AI in financial services. Regulations requiring demonstrated AI behavioral assessment are not coming—they're here. Organizations that can prove compliance will have competitive advantage; those that can't will face penalties, litigation, and market access restrictions.
AI providers certifying their own systems creates the same conflict of interest that led to mandatory independent financial auditing after Enron. The market will demand third-party verification—not because regulators require it, but because customers, partners, and insurers will refuse to accept self-attestation. We're building that verification infrastructure now.
In an era of deepfakes and synthetic media, cryptographic verification is the only way to establish trust at scale. Our hash chains and blockchain anchors provide mathematically unfakeable proof. When we verify an AI's behavioral assessment, that verification cannot be retroactively altered, deleted, or falsified—by us or anyone else.
As more organizations adopt independent certification, it becomes the expected standard. Early adopters establish credibility and operational maturity. Late adopters must explain why they resisted—a conversation no organization wants to have with regulators, customers, or juries.
As AI-related litigation increases, documented behavioral assessment becomes evidence of due diligence. Organizations with certification can demonstrate they took reasonable steps to ensure their AI behaved appropriately. Organizations without it face increased liability exposure and higher insurance premiums.
“The question isn't whether independent AI behavioral verification will become standard—it's who will build it. We're building it now.”
Our patent portfolio represents implemented solutions, but significant research questions remain. We welcome collaboration with academic researchers and AI safety organizations.
How do dimension weightings vary across cultures and domains? What is the predictive validity of assessment scores for real-world behavioral failures?
How do collective dynamics scale? Can manufactured consensus detection be evaded by sophisticated coordination?
What philosophical frameworks best map to the virtue level? How should thresholds be calibrated across levels?
What is the optimal assessment frequency for different risk levels? How can privacy-utility tradeoffs be improved through cryptographic techniques?
We provide detailed research briefs for each patent area and welcome inquiries about data access, methodology collaboration, and academic partnerships.
Contact for research collaboration: greg@gidanc.comU.S. Provisional Patent Application No. 63/949,454
© 2026 GiDanc AI LLC. Patent applications are pending. The innovations described represent our approach to AI behavioral assessment and are subject to ongoing development and refinement.