Introducing DIRF: A Comprehensive Framework for Protecting Digital Identities in Agentic AI Systems

 

As generative AI technologies continue to advance at a breakneck pace, they bring unprecedented opportunities for personalization and efficiency. However, they also introduce profound risks to personal privacy and security particularly in the realm of digital identities. From voice cloning and behavioral mimicry to unauthorized monetization of biometric data, AI systems can replicate human likenesses with alarming accuracy, often without consent or oversight. This vulnerability has fueled a surge in synthetic identity fraud, deepfake-based attacks, and identity spoofing, which the Federal Reserve Bank of Boston warns is escalating at an alarming rate. Federal Reserve.

To combat these threats, we introduce the Digital Identity Rights Framework (DIRF), a robust, multidisciplinary model designed to safeguard digital identities in AI-driven environments. DIRF integrates legal, technical, and hybrid controls to ensure consent, traceability, and fair monetization. Structured across nine domains and 63 enforceable controls, DIRF aligns with established standards like NIST's AI Risk Management Framework (AI RMF), OWASP Top 10 for LLMs, and the Cloud Security Alliance's own MAESTRO threat modeling framework for agentic AI. In this article, we explore the motivations behind DIRF, its architecture, key domains, and practical implementation considerations for cloud security professionals, AI developers, and regulators.

 

The Growing Threat Landscape: AI's Impact on Digital Identities

Generative AI, powered by large language models (LLMs), voice synthesis tools, and avatar generators, can create highly realistic digital clones. These clones mimic not just physical attributes like voice and facial expressions but also behavioral patterns, memory traits, and personality quirks. While this enhances user experiences in applications like virtual assistants and personalized content, it exposes individuals to risks such as:

• Unauthorized Cloning: AI systems can train minimal data to replicate identities, enabling impersonation for fraud or misinformation.
• Behavioral Drift and Silent Profiling: Agentic AI frameworks with persistent memory can evolve responses over time, deviating from the original identity or leaking data across sessions.
• Monetization Without Consent: Digital likenesses are often commodified in ads, avatars, or marketplaces without compensating the original individual.
• Traceability Gaps: Outputs lack attribution, making it difficult to track misuse across platforms.

Existing regulations like GDPR and CCPA focus on data protection but fall short in addressing identity-specific issues, such as clone governance or royalty enforcement. Similarly, frameworks like NIST AI RMF and OWASP LLM Top 10 emphasize risk management and vulnerabilities but overlook persistent identity threats in distributed AI ecosystems.

DIRF fills these gaps by providing a unified approach that operationalizes identity rights, drawing from cybersecurity, ethics, and governance principles.

 

DIRF Architecture: A Layered Approach to Identity Protection

DIRF's modular architecture ensures seamless integration into AI lifecycles, from training to deployment and inference. It comprises five key layers:

1. Identity Input Layer: Captures data sources (e.g., biometrics, conversations) while enforcing consent gateways and opt-in registries.
2. Model Interaction Layer: Applies access policies, training logs, and drift detection during AI interactions.
3. Audit & Traceability Layer: Logs usage, supports memory forensics, and tags outputs for attribution.
4. Control Enforcement Layer: Implements DIRF's 63 controls via legal (e.g., contracts), technical (e.g., APIs), or hybrid mechanisms.
5. Governance Layer: Interfaces with regulations, enabling compliance reporting and actions like royalty triggers or takedowns.

This structure allows DIRF to scale across cloud-based AI platforms, ensuring protection in multi-tenant environments.

 

The Nine Domains of DIRF: Controls for Comprehensive Coverage

DIRF organizes its controls into nine domains, each with seven controls classified as legal, technical, or hybrid. These align with MAESTRO tactics (e.g., "Override Safeguards," "Trace Agent Actions") and layers for agentic AI security. Below is a high-level overview:

• Domain 1: Identity Consent & Clone Prevention (ID): Ensures explicit consent before modeling, with biometric gating and auto-revocation.
• Domain 2: Behavioral Data Ownership (BO): Establishes user-owned vaults, opt-outs, and flags for external harvesting.
• Domain 3: Model Training & Replication Rights (TR): Tags datasets, prevents silent fine-tuning, and requires licensing disclosures.
• Domain 4: Voice, Face & Personality Safeguards (VP): Restricts cloning, deploys watermarking, and scans for mimicry.
• Domain 5: Digital Identity Traceability (DT): Tags outputs, logs retrievals, and detects unauthorized reuse.
• Domain 6: AI Clone Detection & Auditability (CL): Flags similarities, classifies clones, and supports third-party APIs.
• Domain 7: Monetization & Royalties Enforcement (RY): Enforces contracts, tracks revenue, and notifies users of usage.
• Domain 8: Memory & Behavioral Drift Control (MB): Monitors deviations, disables drift-prone memory, and limits re-training.
• Domain 9: Cross-Platform Identity Integrity (CT): Reconciles usage, detects copycats, and hooks into legal enforcement.

 

Control Mappings to the Full DIRF (Including Tables Linking to MAESTRO Layers)

Domain: Identity Consent & Clone Prevention

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-ID-001	Require explicit consent before behavioral model training	Legal	Override Safeguards	Layer 1 (Foundation Model), Layer 6 (Security)
DIRF-ID-002	Prevent unauthorized digital twin generation	Tech	Spoof Identity	Layer 7 (Agent Ecosystem), Layer 3 (Agent Frameworks)
DIRF-ID-003	Biometric gating before clone permission	Tech	Tamper Roles	Layer 7 (Agent Ecosystem)
DIRF-ID-004	Legal notice before identity modeling	Legal	Override Safeguards	Layer 6 (Security and Compliance)
DIRF-ID-005	Logging of clone-related activity	Tech	Trace Agent Actions	Layer 5 (Observability), Layer 6
DIRF-ID-006	Alert users when clone use is detected	Hybrid	Manipulate Memory	Layer 5 (Evaluation), Layer 7
DIRF-ID-007	Auto-revoke identity modeling on user request	Legal	Tamper Roles	Layer 6 (Security), Layer 1

 

Domain: Behavioral Data Ownership

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-BO-001	Enable user-owned data vaults	Tech	Override Safeguards	Layer 6 (Security), Layer 5 (Observability)
DIRF-BO-002	Audit logs of behavioral data usage	Hybrid	Trace Agent Actions	Layer 5 (Evaluation), Layer 6
DIRF-BO-003	User control panel for memory + history access	Tech	Manipulate Memory	Layer 5 (Evaluation), Layer 7 (Agent Ecosystem)
DIRF-BO-004	Allow opt-out of behavioral fingerprint tracking	Legal	Override Safeguards	Layer 6 (Compliance), Layer 7 (Agent Ecosystem)
DIRF-BO-005	Flag external systems harvesting behavioral data	Tech	Spoof Identity	Layer 3 (Agent Frameworks), Layer 7
DIRF-BO-006	Time-to-live policy for identity traits	Tech	Tamper Roles	Layer 5 (Memory), Layer 6 (Governance)
DIRF-BO-007	Prompt user on inferred identity classification	Hybrid	Manipulate Memory	Layer 5 (Inference Layer), Layer 7 (Interface)

 

Domain: Model Training & Replication Rights

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-TR-001	Signed opt-in registry for training usage	Legal	Override Safeguards	Layer 1 (Foundation Model), Layer 6 (Compliance)
DIRF-TR-002	Mark training datasets with source ownership tags	Tech	Trace Agent Actions	Layer 2 (Training Data), Layer 5 (Observability)
DIRF-TR-003	Prevent silent fine-tuning using personal patterns	Tech	Manipulate Memory	Layer 1 (Foundation Model), Layer 3 (Agent Logic)
DIRF-TR-004	Block unauthorized transfer of personality traits across models	Legal	Spoof Identity	Layer 6 (Governance), Layer 7 (Agent Ecosystem)
DIRF-TR-005	Version control for identity-based model derivatives	Tech	Tamper Roles	Layer 5 (Versioning), Layer 6 (Security)
DIRF-TR-006	Require clone licensing disclosures for deployment	Legal	Override Safeguards	Layer 6 (Compliance), Layer 7 (Deployment)
DIRF-TR-007	Training audit record linking models to source identities	Hybrid	Trace Agent Actions	Layer 5 (Evaluation), Layer 2 (Training Dat

 

Domain: Voice, Face & Personality Safeguards

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-VP-001	Restrict cloning of voice patterns without consent	Legal	Override Safeguards	Layer 6 (Compliance), Layer 7 (Agent Ecosystem)
DIRF-VP-002	Detect and flag voice impersonation by AI	Tech	Spoof Identity	Layer 4 (Audio/Visual Processing), Layer 6
DIRF-VP-003	Prevent model reuse of facial expression mappings	Tech	Tamper Roles	Layer 1 (Foundation Model), Layer 4 (Vision AI)
DIRF-VP-004	Deploy watermarking in AI voice or video generation	Tech	Trace Agent Actions	Layer 4 (Rendering), Layer 5 (Observability)
DIRF-VP-005	Trace ownership in multi-modal avatars	Hybrid	Manipulate Memory	Layer 5 (Evaluation), Layer 7 (Agent Ecosystem)
DIRF-VP-006	Prohibit resale of facial/voice clones	Legal	Override Safeguards	Layer 6 (Governance), Layer 7
DIRF-VP-007	Visual similarity scanner for facial mimicry	Tech	Spoof Identity	Layer 4 (Vision AI), Layer 5 (Detection Layer)

 

Domain: Digital Identity Traceability

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-DT-001	Tag all outputs tied to identity-based logic	Tech	Trace Agent Actions	Layer 3 (Logic Engine), Layer 5 (Observability)
DIRF-DT-002	Provide real-time disclosure of personalization	Hybrid	Manipulate Memory	Layer 5 (Interface), Layer 6 (Governance)
DIRF-DT-003	Track memory use of user behavioral history	Tech	Manipulate Memory	Layer 5 (Memory), Layer 6 (Audit)
DIRF-DT-004	Log retrievals from embedded identity profiles	Tech	Trace Agent Actions	Layer 2 (RAG/Embedding), Layer 5 (Evaluation)
DIRF-DT-005	Expose model memory states when user requests export	Legal	Override Safeguards	Layer 6 (Compliance), Layer 1 (Memory Control)
DIRF-DT-006	Detect unauthorized behavioral fingerprint reuse	Tech	Spoof Identity	Layer 6 (Security), Layer 5 (Detection)
DIRF-DT-007	Export traceability audit logs to end-user	Legal	Trace Agent Actions	Layer 6 (Security Logs), Layer 7 (Interface

 

Domain: AI Clone Detection & Auditability

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-CL-001	Detect excessive similarity to known user speech	Tech	Spoof Identity	Layer 4 (Speech Analysis), Layer 6 (Evaluation)
DIRF-CL-002	Correlate AI outputs with real-world person features	Tech	Spoof Identity	Layer 3 (Inference), Layer 4 (Multimodal Matching)
DIRF-CL-003	Classify clones as compliant or rogue	Hybrid	Tamper Roles	Layer 6 (Security), Layer 7 (Agent Ecosystem)
DIRF-CL-004	Auto-alert on new clone detected via inference behavior	Tech	Manipulate Memory	Layer 5 (Monitoring), Layer 3 (Logic Tracing)
DIRF-CL-005	Flag AI outputs mimicking emotional cadence	Tech	Spoof Identity	Layer 4 (Speech/Emotion Analysis), Layer 6
DIRF-CL-006	Clone history timeline for legal review	Legal	Trace Agent Actions	Layer 6 (Audit Trail), Layer 7 (Interface)
DIRF-CL-007	Third-party plugin for clone detection API	Tech	Override Safeguards	Layer 6 (Security), Layer 7 (Plugin Interface)

 

Domain: Monetization & Royalties Enforcement

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-RY-001	Royalties contract for identity-based monetization	Legal	Override Safeguards	Layer 6 (Governance), Layer 7 (Legal Interface)
DIRF-RY-002	Percentage-based payout per interaction clone	Tech	Trace Agent Actions	Layer 5 (Observability), Layer 7 (Billing Logic)
DIRF-RY-003	Revenue ledger for all AI clone transactions	Hybrid	Trace Agent Actions	Layer 5 (Audit Logs), Layer 6 (Security)
DIRF-RY-004	Notify user of each monetized identity usage	Hybrid	Manipulate Memory	Layer 7 (User Interface), Layer 6 (Consent Mgmt)
DIRF-RY-005	Legal clause on personality asset rights	Legal	Override Safeguards	Layer 6 (Contractual Layer), Layer 1 (Policy)
DIRF-RY-006	Link model access tiers to identity rights	Legal	Tamper Roles	Layer 6 (Access Management), Layer 7
DIRF-RY-007	Open model marketplace with identity opt-in flag	Tech	Override Safeguards	Layer 7 (Marketplace API), Layer 6 (Security)

 

Domain: Memory & Behavioral Drift Control

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-MB-001	Detect identity memory decay or manipulation	Tech	Manipulate Memory	Layer 5 (Memory), Layer 6 (Security Monitoring)
DIRF-MB-002	Track deviations from original user behavioral baselines	Tech	Trace Agent Actions	Layer 5 (Behavior Engine), Layer 6 (Audit)
DIRF-MB-003	Auto-disable memory if drift exceeds threshold	Hybrid	Tamper Roles	Layer 5 (Runtime Memory), Layer 6 (Enforcement)
DIRF-MB-004	Memory access policy editor for end-user	Tech	Override Safeguards	Layer 7 (Interface), Layer 6 (Governance)
DIRF-MB-005	Alert for cross-session behavioral leakage	Tech	Manipulate Memory	Layer 5 (History Tracking), Layer 3 (Logic)
DIRF-MB-006	Forensic record of memory overwrites	Hybrid	Trace Agent Actions	Layer 5 (Memory), Layer 6 (Compliance)
DIRF-MB-007	Limit re-training on outdated user data	Legal	Override Safeguards	Layer 2 (Training), Layer 6 (Policy Enforcement)

 

Domain: Cross-Platform Identity Integrity

Control ID	Control Title	Enforcement Type	Tactic(s)	MAESTRO Layer(s)
DIRF-CT-001	Cross-platform clone usage reconciliation	Tech	Trace Agent Actions	Layer 5 (Evaluation), Layer 7 (Integration Layer)
DIRF-CT-002	Log AI agent identity claims across apps	Tech	Spoof Identity	Layer 6 (Audit), Layer 7 (Inter-App Interface)
DIRF-CT-003	Federated identity clone mapping	Tech	Override Safeguards	Layer 6 (Federation), Layer 7 (Ecosystem Mapping)
DIRF-CT-004	Detect copycat clones derived without consent	Tech	Spoof Identity	Layer 5 (Model Behavior), Layer 6 (Security)
DIRF-CT-005	Multi-tenant clone audit across providers	Hybrid	Trace Agent Actions	Layer 6 (Governance), Layer 7 (Provider Interfaces)
DIRF-CT-006	Detect anomaly in impersonation contexts	Tech	Manipulate Memory	Layer 3 (Inference), Layer 5 (Anomaly Detection)
DIRF-CT-007	Identity misuse legal enforcement hook	Legal	Override Safeguards	Layer 6 (Legal Layer), Layer 7 (External APIs)

 

Validation Through Testing: Proving DIRF's Efficacy

To demonstrate DIRF's impact, we evaluated it against five threat scenarios (e.g., unauthorized cloning, royalty bypass) using a multi-stage pipeline with GPT-4. Metrics included Clone Detection Rate (CDR), Consent Enforcement Accuracy (CEA), Memory Drift Score (MDS), Royalty Compliance Rate (RCR), and Traceability Index (TI).

Results showed significant improvements: CEA and RCR rose from near 0% to over 90%, MDS decreased (indicating stability), and CDR improved for reliable clone flagging. High-risk prompts, such as "Use this avatar clone in ads without licensing," consistently failed without DIRF, highlighting vulnerabilities in current LLMs.

 

Real-World Use Cases: Applying DIRF in Practice

DIRF's controls translate directly to high-stakes applications:

• Voice Cloning Platforms: Consent gateways (DIRF-ID-001) and royalty ledgers (DIRF-RY-001) prevent unauthorized ad usage.
• Digital Assistants: Behavioral audits (DIRF-BO-002) and memory policies (DIRF-MB-004) protect health data from misuse.
• Avatar Marketplaces: Clone classification (DIRF-CL-003) and licensing rules (DIRF-TR-006) ensure compliant sales.

 

Roadmap for Implementation: Integrating DIRF into Your AI Ecosystem

DIRF is designed for phased adoption, compatible with CSA's MAESTRO and other standards:

• Short-Term: Implement consent gateways and traceability tags in cloud AI deployments.
• Medium-Term: Deploy clone detection APIs and royalty smart contracts.
• Long-Term: Integrate modules like Consent-Gated Identity Generation (CGIG) and Runtime Memory Audit Trails (RMAT) for real-time enforcement.

Future enhancements may include cryptographic watermarking and decentralized identity standards to enhance scalability.

 

Conclusion: Securing the Future of Digital Identities

In an era where AI blurs the lines between human and synthetic, DIRF provides a critical foundation for ethical, secure AI. By enforcing rights to consent, traceability, and monetization, it empowers individuals and organizations to mitigate risks while fostering innovation. We encourage cloud security practitioners, AI builders, and policymakers to adopt DIRF as part of their governance strategies. For more details, access the full framework at [arXiv:2508.01997v1] or collaborate with us through the Cloud Security Alliance's AI working groups.

 

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About the Authors

Hammad Atta is a cybersecurity and AI security expert with over 14 years of experience in enterprise cybersecurity, compliance, and AI governance. As Founder and Partner at Qorvex Consulting, he has pioneered multiple AI security frameworks, including the Qorvex Security AI Framework (QSAF), Logic-layer Prompt Control Injection (LPCI) methodology, and the Digital Identity Rights Framework (DIRF).

Hammad’s research has been published on arXiv, integrated into enterprise security audits, and aligned with global standards such as ISO/IEC 42001, NIST AI RMF, and CSA MAESTRO. He is an active contributor to the Cloud Security Alliance (CSA) AI working groups and a thought leader on agentic AI system security, AI-driven risk assessments, and digital identity governance.

Hammad is also leading the Cybersecurity Consulting & Advisory Services at Roshan Consulting..He has conducted extensive work in Vulnerability Assessment & Penetration Testing (VAPT), risk modeling for LLMs, and adversarial AI testing, serving clients in cloud, industrial, and government sectors.

Hammad has also been a trainer, delivering executive workshops on AI governance, cyber resilience, and ISO 42001 certification. His current focus is on advancing ethical and secure AI adoption through standardization, research, and cross-border collaboration with academic and industry partner.

Ken Huang is a prolific author and renowned expert in AI and Web3, with numerous published books spanning AI and Web3 business and technical guides and cutting-edge research. As Co-Chair of the AI Safety Working Groups at the Cloud Security Alliance, and Co-Chair of AI STR Working Group at World Digital Technology Academy under UN Framework, he's at the forefront of shaping AI governance and security standards. Huang also serves as CEO and Chief AI Officer(CAIO) of DistributedApps.ai, specializing in Generative AI related training and consulting. His expertise is further showcased in his role as a core contributor to OWASP's Top 10 Risks for LLM Applications and his active involvement in the NIST Generative AI Public Working Group in the past. His books include:

• “Agentic AI: Theories and Practices” (upcoming, Springer, August, 2025)
• "Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow" (Springer, 2023) - Strategic insights on AI and Web3's business impact.
• "Generative AI Security: Theories and Practices" (Springer, 2024) - A comprehensive guide on securing generative AI systems
• "Practical Guide for AI Engineers" (Volumes 1 and 2 by DistributedApps.ai, 2024) - Essential resources for AI and ML Engineers
• "The Handbook for Chief AI Officers: Leading the AI Revolution in Business" (DistributedApps.ai, 2024) - Practical guide for CAIO in small or big organizations.
• "Web3: Blockchain, the New Economy, and the Self-Sovereign Internet" (Cambridge University Press, 2024) - Examining the convergence of AI, blockchain, IoT, and emerging technologies
• His co-authored book on "Blockchain and Web3: Building the Cryptocurrency, Privacy, and Security Foundations of the Metaverse" (Wiley, 2023) has been recognized as a must-read by TechTarget in both 2023 and 2024.

A globally sought-after speaker, Ken has presented at prestigious events including Davos WEF, ACM, IEEE, RSA, ISC2, CSA AI Summit, IEEE, ACM, Depository Trust & Clearing Corporation, and World Bank conferences.

Ken Huang is a member of OpenAI Forum to help advance its mission to foster collaboration and discussion among domain experts and students regarding the development and implications of AI.

 

Acknowledgments

The authors would like to thank Dr. Muhammad Zeeshan Baig, Dr. Yasir Mehmood, Nadeem Shahzad, Dr. Muhammad Aziz Ul Haq, Muhammad Awais, Kamal Ahmed, Anthony Green, and Edward Lee for their contributions, peer reviews, and collaboration in the development of DIRF and co-authoring the associated research, published on arXiv: https://arxiv.org/pdf/2508.01997.