Abstract
Technical Subtitle: Hardware-Attested AI Execution Governance, Algorithmic Logic Fingerprinting, Runtime Behavioral Verification, Multi-Authority Authorization, and Cryptographically Enforced Output Finality for Sovereign AI, Cloud Infrastructure and Autonomous Agents.
Indian Patent Application No.: 202631049021
Original Filing Date: 17 April 2026
Foundational International Application: PCT/IB2026/055615 — Filed 4 June 2026.
International Publication: WO 2026/150382 A2 — Published 16 July 2026.
Public Disclosure and Independent Verification: The inventor identifies the original Indian filing dated 17 April 2026 as publicly accessible through the Priority Documents section of the Mothership application PCT/IB2026/055615 on WIPO PATENTSCOPE. Patent examiners, researchers and engineering teams may independently cross-check the original filing, specification and claims against the associated international application records.
WIPO Reference: https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026150382
Industry Relevance: OpenAI · Anthropic · Microsoft · Google DeepMind · Amazon Web Services · NVIDIA · AMD · Intel · Qualcomm · IBM · Apple · Meta · Alibaba Cloud · Huawei Cloud · Tencent Cloud · Baidu · DeepSeek · Moonshot AI · Zhipu AI · SAP · Mistral AI · OVHcloud · Nokia · Ericsson · Deutsche Telekom · Orange.
Abstract
As artificial intelligence moves from generating answers to executing autonomous workflows, invoking external tools, processing regulated information and producing consequential decisions across national boundaries, a critical security question emerges: who verifies that the approved algorithm actually executed, that its runtime behavior remained within authorized conditions, and that the resulting output may lawfully and technically become effective in the intended destination?
This challenge is increasingly relevant to global AI ecosystems, including OpenAI's GPT models and agent APIs, Anthropic's Claude and tool-use infrastructure, Microsoft's Copilot and Foundry Agent Service, Google's Gemini Enterprise Agent Platform and Model Armor, Amazon Bedrock AgentCore Policy and Gateway, and NVIDIA's confidential computing infrastructure. These systems illustrate environments in which model identity, runtime execution, tool authorization and externally consequential output can require independently verifiable governance.
Comparable technical requirements arise across Alibaba Cloud's Qwen and Model Studio, Huawei Cloud's Pangu and AgentArts, Tencent's Hunyuan and Agent Runtime, Baidu's ERNIE and Qianfan, SAP's Joule and Joule Studio, Mistral AI's enterprise agent platforms, and European sovereign-cloud infrastructure. Their possible relevance includes regulated AI inference, cross-border enterprise data processing, autonomous agent operations and jurisdiction-sensitive output delivery.
This technical disclosure, based on Indian Patent Application No. 202631049021, filed 17 April 2026, describes a four-layer cascading cryptographic governance architecture operating at the level of each individual AI inference invocation.
Layer 1 — Algorithm Authorization: An Algorithmic Logic Fingerprint (ALF) identifies the proposed executing algorithm and is verified against a pre-bound fingerprint and approved algorithm registry before inference.
Layer 2 — Runtime Behavioral Verification: Predefined instrumentation points collect governance-relevant execution signals, including policy activation, safety triggers, tool and retrieval activity, model version, risk tier, purpose and workflow state. These signals are normalized into a deterministic Runtime Behavioral Governance Descriptor incorporating the verified ALF.
Layer 3 — Cryptographic Jurisdiction Binding: The runtime descriptor is cryptographically bound to a structured jurisdiction context identifying the source, destination, applicable legal-basis reference and invocation-specific nonce. The resulting binding commitment is signed within a Trusted Execution Environment, Hardware Security Module or equivalent protected enforcement domain using classical and post-quantum cryptographic signatures.
Layer 4 — Protected Output Finality: Before an output becomes externally effective, a finality controller verifies the required cryptographic signatures, approved behavioral and purpose classes, anti-replay evidence, destination jurisdiction credentials and applicable authority approvals. Cross-border authorization may employ independently signed source- and destination-authority approval objects, including pre-computed bilateral corridors for eligible high-volume deployments.
The architecture further distinguishes LAVR_ALF, recording algorithm authorization, from LAVR_BEHAVIORAL, recording the output-release decision. These independently inspectable, cryptographically linked validation records provide an end-to-end governance chain from approved algorithm entry to authorized output release.
Unlike a proposal to replace global internet routing or require every AI computation to execute inside secure hardware, the disclosed architecture supports integration through existing inference orchestration, protected signing services, enterprise AI gateways and participating enforcement boundaries. Infrastructure credentials provide verifiable jurisdictional attributes under defined trust policies rather than assuming IP addresses or organizational certificates alone establish physical location.
Its potential technical value lies in making the release or downstream acceptance of high-consequence AI outputs dependent on verifiable authorization evidence. It does not prevent independently controlled computation outside participating enforcement systems, nor does a cryptographic reference replace legal interpretation or establish regulatory compliance by itself.
The central contribution is a proposed transition from deployment-level AI governance and after-the-fact audit toward per-invocation, cryptographically linked verification of algorithm identity, observed runtime behavior, authorized jurisdiction and final output effectuation.
The disclosed mechanisms may be relevant to future AI-agent security, sovereign AI infrastructure, enterprise cloud services, telecommunications and AI-native 6G networks, banking, industrial automation and critical infrastructure, subject to independent implementation, latency, interoperability and security validation.
Keywords
AI Safety; AI Agent Security; Execution Finality; Per-Invocation AI Governance; Algorithmic Logic Fingerprint (ALF); Runtime Behavioral Governance Descriptor; Cryptographic Behavioral-Jurisdiction Binding; Cascading Fingerprint Architecture; Protected Output Finality; Hardware-Enforced Authorization; Multi-Authority Co-Signing; Bilateral Corridor Authorization; Ledger-Anchored Validation Record; LAVR_ALF; LAVR_BEHAVIORAL; Trusted Execution Environment; Hardware Security Module; Confidential Computing; Remote Attestation; Post-Quantum Cryptography; ML-DSA; Zero Trust AI; Model Provenance; Runtime Policy Enforcement; AI Gateway; Sovereign AI; Sovereign Cloud; Cross-Border AI Governance; Non-IP Jurisdiction Verification; Infrastructure Credentials; GDPR Chapter V; Cryptographic Provenance; Autonomous AI Agents; AI-Native 6G; Critical Infrastructure Security.
Technical Alignment and Non-Infringement Disclaimer
Company and product references identify possible areas of technical relevance, architectural integration and future engineering research. They do not imply implementation, adoption, endorsement, collaboration, infringement or licensing obligations. The architecture's actual performance, non-bypassability and interoperability require independent technical validation.
Inventorship and AI-Assistance Statement
The underlying invention is human-originated. AI assistance was used to prepare this explanatory title, abstract, industry terminology and product-relevance discussion. The original filed specification and claims remain the authoritative source.
Original Indian Filing: 202631049021 — 17 April 2026.
Foundational International Publication: WO 2026/150382 A2 — 16 July 2026.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "When AI Crosses Borders, Who Authorizes Its Final Output? From GPT and Claude to Gemini, Qwen and Mistral — Cryptographic Proof of Algorithm, Behavior and Jurisdiction", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/13011