Responsible AI Policy

Effective Date: 2026-06-03
Entity: PsyData Labs L.L.C. (PDL)

PsyData Labs L.L.C. (PDL) maintains an enterprise-grade AI governance program mapped to the highest global standards, including the NIST AI Risk Management Framework (NIST AI RMF 1.0), the EU AI Act, ISO/IEC 42001:2023, GDPR, CPRA, and the NY SHIELD Act. This Responsible AI Policy outlines our comprehensive commitments and operational constraints for the ethical, safe, and transparent development and deployment of artificial intelligence systems.

1. Core Principles and Regulatory Alignment

Our approach to AI is grounded in defense-in-depth controls across people, process, and technology. We map our obligations strictly to the following standards:

• NIST AI RMF 1.0: Continuous governance through the GOVERN, MAP, MEASURE, and MANAGE functions, mapping all evaluation gate controls accordingly.
• EU AI Act (Arts. 9, 14, 73): Strict risk tiering, mandatory Human-in-the-Loop (HITL) oversight for high-risk systems, prohibited practice screening, and 15-day serious incident reporting to market surveillance authorities.
• Privacy Standards (GDPR, CPRA, NY SHIELD): Upholding data subject rights (DSARs), maintaining lawful basis processing matrices, and enforcing strict 30-to-45-day SLAs for access, correction, and deletion requests.
• Security Standards: Maintaining ISO/IEC 27001/27701 and SOC 2 Type II alignment with robust incident response playbooks.

2. AI Risk Tiering and Human Oversight

All AI models and data pipelines at PDL are subject to rigorous risk tiering prior to deployment.

• Pre-Deployment Assessment: Every system must be assigned a documented AI risk tier before entering production environments. All production models must maintain a current model card.
• Human-in-the-Loop (HITL): PDL strictly prohibits fully automated decisions concerning employment, credit, insurance, or access to critical services based solely on AI inferences (D-INF). Any High-Risk Psychological Profile (H-RP) inference affecting Restricted (S3+) data subjects mandates HITL sign-off and a documented appeal path.
• Kill-Switch Procedures: We maintain active kill-switch protocols for endpoints exhibiting anomalous harm rates, allowing immediate automated or manual rollback if risk thresholds are breached.
• Third-Party Model Vetting: Third-party foundation models used for incident learning, rollback, or core evaluation must be reviewed for safety and license restrictions prior to integration.

3. Bias Mitigation, Fairness, and Output Safety

PDL actively prevents and remediates algorithmic bias to ensure equitable outcomes.

• Adversarial Evaluation Gates: Models must pass rigorous pre-deployment testing against adversarial and edge-case suites to identify disparate impacts and vulnerability to prompt injection.
• Continuous Monitoring: We enforce continuous drift detection and bias monitoring. Rollbacks are automatically initiated when established disparate impact thresholds are breached.
• Harm Filters: Robust output filters are implemented across inference paths to automatically block harmful, discriminatory, or unauthorized generations.
• Model Explainability: We provide explainability metadata and summaries for evaluation gate outputs when required by contract or regulation, ensuring transparency in AI-driven decisions.

4. Synthetic Data and Memorization Prevention

To protect individual privacy and prevent the memorization and leakage of sensitive data (including Behavioral and Psychological signals), PDL mandates strict synthetic data controls.

• Memorization Prevention: Training pipelines prioritize the use of synthetic data—artificially generated records statistically similar to production sets—to minimize the risk of memorizing actual user data and protecting behavioral privacy.
• Watermarking: All synthetic datasets used in training, documentation, and incident learning must be cryptographically or statistically watermarked to ensure provenance tracking.
• Production Isolation: Synthetic data is strictly barred from production decisioning systems without an independent risk review. Furthermore, watermarked synthetic records are systematically excluded from Data Subject Access Request (DSAR) production systems unless explicitly disclosed.

5. Behavioral and Psychological Data Safeguards

Given the sensitivity of our data domains, PDL enforces specialized controls over Behavioral Data (D-BEH) and Psychological Signals (D-PSY).

• Advertising Prohibition: Raw psychological signals (D-PSY) shall never be used for marketing or advertising targeting under any circumstances.
• Consent and Repurposing: The collection of behavioral data is strictly limited to disclosed purposes. Any research repurposing of behavioral logs requires renewed, explicit consent or an updated, compatible lawful basis.
• Non-Production Restrictions: Real psychological signals are strictly barred from non-production environments (e.g., staging, training, or corporate SaaS endpoints) without comprehensive scrubbing and anonymization, unless specifically tagged as S1.

6. Incident Response and Accountability

We maintain an aggressive, transparent posture on AI incident response and lifecycle accountability.

• Internal Reporting: Any serious AI incident or anomalous model behavior must be reported to the Compliance Lead and Security Lead within 24 hours.
• Regulatory Notification: For applicable high-risk AI systems, serious incidents are reported to market surveillance authorities within 15 days (EU AI Act). GDPR (72 hours), CPRA, and NY SHIELD Act breach notifications are executed strictly within statutory timelines.
• Audit and Logging: Centralized logs of prompts, outputs, and evaluation metadata for high-risk systems are retained to support incident learning and accountability. Security event logs are retained for ≥400 days, while consent and DSAR actions are retained for ≥24 months.

7. Exceptions and Continuous Improvement

Any deviations from this policy require formal written requests via the Security/Privacy exception forms, including a comprehensive risk assessment, compensating controls, and executive sign-off (Managing Member approval is mandatory for S3+ environments). Exceptions strictly expire within 90 days. We conduct quarterly internal audits sampling policy requirements and an annual tabletop exercise for psychological-signal misuse to ensure our AI governance program continuously evolves alongside emerging risks.

For inquiries regarding this policy or our AI governance practices, please contact compliance@psydatalabs.com.