Operational standards and technical controls for responsible AI usage and client data protection.
This AI Data Protection Framework serves as the operational companion to Industry Intelligence Inc.'s Data Security & Governance Policy, detailing the specific frameworks, guidelines, and technical controls we implement to ensure responsible AI usage and client data protection. While the Data Security & Governance Policy establishes our overarching security commitments and principles, this Framework provides the practical implementation standards and procedures that enforce those policies within our AI-enabled systems.
This document explains how Industry Intelligence Inc. ("IndustryIntel") integrates artificial intelligence into our products and services while maintaining the highest standards of data protection, privacy, and security for our clients.
Client content (prompts, files, chats, outputs, and metadata) is not used to train or fine‑tune foundational models unless expressly authorized in a contract. We configure vendors to disable provider‑side training where controls exist; if "no‑training" cannot be contractually guaranteed, we do not use that vendor for client data.
Client data is processed strictly to deliver contracted services (e.g., summarization, tagging, retrieval‑augmented Q&A) with logical and technical separation by customer/tenant.
We prefer retrieval over retention, avoid personal data where feasible, and apply privacy‑enhancing techniques (redaction, tokenization, aggregation) consistent with Privacy‑Enhanced design.
AI‑assisted outputs are labeled. We preserve and display source links for generated summaries/answers and, where practical, propagate provenance signals (e.g., watermarks/signatures).
Analysts can review, challenge, and override system outputs; we expose limitations, confidence cues, and citations to support Explainable/Interpretable use.
Encryption in transit/at rest, least privilege, MFA, secure SDLC, and monitoring/red‑teaming against AI‑specific threats (prompt injection, data poisoning, model misuse).
We measure and mitigate harmful bias and performance disparities and document context/limitations, consistent with Fair — with harmful bias managed.
We maintain AI incident response and disclosure processes (including after‑action reviews and logging) appropriate to severity and context.
We operationalize NIST's AI RMF Core — GOVERN, MAP, MEASURE, MANAGE — and its trustworthiness characteristics (valid & reliable; safe; secure & resilient; accountable & transparent; explainable & interpretable; privacy‑enhanced; fair with harmful bias managed).
Only what is necessary to deliver features. Prefer client‑side redaction or server‑side tokenization. No unrelated mining or secondary use.
Tenant‑level segregation; strict least‑privilege access, auditable logs. Context data (prompts/retrieved passages/outputs) retained only as needed for delivery, support, and safety.
Encryption in transit/at rest; short, contract‑controlled retention for logs and chats; secure destruction on decommission.
Show sources and timestamps for AI‑assisted outputs; label generated content.
Evaluate performance across segments (languages, dialects, geographies, user cohorts); document limitations; use diverse reviewers and counter‑prompt tests.
Threat modeling; defense‑in‑depth against direct/indirect prompt injection; validation/sanitization of external content prior to model use; adversarial testing; detection of PII leakage; value‑chain hardening.
Contracts cover "no training," IP/content ownership, incident SLAs, provenance expectations, and audit rights; maintain approved‑provider list and monitor changes.
On request: disable transcript storage; enforce stricter pre‑call redaction; custom retention.
Teammates are AI‑assisted modules that analyze domain‑specific corpora (e.g., market, regulatory, trade, patents, supply chain, economy, and other future domains) and provide retrieval‑augmented answers, summaries, tagging, and impact analysis for client workflows.
Disclosures. Teammates are AI‑assisted research/analysis tools—not legal, investment, or other professional advice. Users should verify with cited primary sources.
Retrieval‑augmented generation from curated sources; answers require citations; confidence/recency cues; block or flag unsupported claims for human review.
Client prompts/uploads remain within the client tenant; excluded from provider/model training by default.
Hardened against prompt injection and tool misuse; adversarial testing and sandboxing; fallbacks and rollbacks defined.
Uniform templates for impact narratives; diverse reviewer QA; measure and remediate disparities across domains and languages.
Supplier risk assessment for datasets, models, and tools; contracts mandate content provenance and incident duties; inventory upstream dependencies and maintain traceability.
Material misrepresentations or provenance failures are logged as AI incidents; we correct outputs and notify affected users as appropriate.
Policies, inventory, roles/training, third‑party controls, and incident governance (AI RMF Table 1).
For each application/Teammate, document purpose, context, data/rights/provenance, foreseeable misuse, and GAI‑specific risks (confabulation, information integrity, privacy, security, harmful bias/homogenization, value‑chain).
TEVV plans for factuality, bias, robustness, explainability, and provenance efficacy; structured feedback and monitoring (AI RMF Table 3; AI 600‑1 actions).
Risk‑based go/no‑go, kill‑switches, rollbacks, incident response & disclosure, and continuous improvement (AI RMF Table 4; AI 600‑1 incident guidance).
The following documents are directly relevant to this policy, and are referenced within this document:
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