Glossary
Key terms for navigating governed AI, privacy engineering, and enterprise compliance.
Each definition includes why it matters for your procurement and deployment decisions.
A
AYITA REALM
Governed runtime for regulated AI work. AI agents and people work inside enforced policy, with governed memory (MARM), deterministic context assembly, and an audit-grade trace of every consequential action - deployed inside your security perimeter.
Why it matters: Unlike cloud AI, AYITA keeps your data sovereign while providing enterprise-grade memory and auditability that compliance teams can verify.
C
Cognitive Trace REALM
Per-response audit record capturing which sources, memories, and decision logic contributed to each AI output. Two-tier architecture: compliance-grade (7-year retention, no content) and debug (7-90 days, encrypted).
Why it matters: When regulators ask "why did the AI say that?", you have a complete, tamper-evident answer - not a black box.
CIO / CISO / CTO
Chief Information Officer, Chief Information Security Officer, Chief Technology Officer. Key executive stakeholders for enterprise AI decisions - CIO owns IT strategy, CISO owns security/risk, CTO owns technology architecture.
Why it matters: Different buyers have different concerns. CIO cares about integration, CISO about data protection, CTO about technical fit. Speak their language.
Context Navigator REALM
AYITA component that assembles context for each query from multiple sources (memory, knowledge base, dialog history) with policy filtering, ranking, and token budget management.
Why it matters: Deterministic context assembly means identical inputs produce identical prompts - auditable, reproducible, debuggable.
D
Differential Privacy (DP)
Mathematical framework providing provable privacy guarantees by adding calibrated noise to data or queries. Prevents individual identification while preserving statistical utility.
Why it matters: Moves privacy from "trust us" to "mathematically proven" - a defensible position when regulators or auditors question your data handling.
DPO (Data Protection Officer)
Role mandated by GDPR responsible for data privacy compliance, conducting DPIAs, and serving as liaison with supervisory authorities.
Why it matters: This person often has veto power over AI deployments. Evidence artifacts should directly address their compliance checklist.
DP-Risk REALM
Privacy risk scoring mechanism that controls both what enters persistent memory and what appears in responses. Unified score drives OPEN/GUARDED/RESTRICTED policy modes across the system.
Why it matters: Automated privacy gating means sensitive data doesn't leak through AI responses - without requiring manual review of every interaction.
DPIA (Data Protection Impact Assessment)
Formal assessment required by GDPR before processing that poses high risk to individuals. Documents data flows, risks, and mitigation measures.
Why it matters: Required for most enterprise AI deployments in EU. Having a DPIA template pre-filled with your architecture accelerates approvals.
Deterministic Context REALM
Architectural guarantee that identical query + retrieval snapshot + policy settings produce byte-identical prompts. Verified via cryptographic hash of assembled context.
Why it matters: Reproducibility enables debugging, audit, and regression testing. "Why did AI say X?" becomes answerable, not a mystery.
E
Evidence Sprint REALM
Time-boxed engagement (30-45 days) producing audit-ready evidence artifacts: privacy impact assessments, risk documentation, architecture diagrams, and test results for AI deployments.
Why it matters: Compresses months of compliance preparation into weeks - with deliverables that directly answer procurement and regulatory questions.
Evidence Pack REALM
The PAMOLA Evidence Pack is the deliverable bundle from an Evidence Sprint, packaged for a specific reviewer (TPRM, Legal, Privacy, Security). It records what operations were performed on the data, what safeguards were applied and why, the quantified tradeoffs with methodology, how data moved (or didn't) through the system, and the path from pilot to production with criteria. Not to be confused with a hardware-attested runtime receipt: this is privacy-engineering evidence with attack-simulation results, independently recomputable.
Why it matters: Different reviewers need different evidence. Pre-packaged bundles mean each stakeholder gets exactly what they need to approve.
EU AI Act
European Union regulation (2024/1689) establishing the first comprehensive legal framework for AI. Classifies AI systems by risk level and imposes requirements on high-risk systems including documentation, transparency, and human oversight.
Why it matters: High-risk AI rules apply from August 2026. Organizations deploying AI in EU need compliant architecture now - retrofitting is expensive.
G
GDPR (General Data Protection Regulation)
EU regulation governing personal data processing. Requires lawful basis, data minimization, purpose limitation, and grants individuals rights including access, erasure, and portability.
Why it matters: The baseline for privacy compliance in EU and beyond. AI systems processing personal data must demonstrate GDPR compliance - especially for cross-border deployments.
GPAI (General-Purpose AI)
EU AI Act term for AI models trained on broad data that can perform diverse tasks (like LLMs). Subject to transparency requirements; models with "systemic risk" face additional obligations.
Why it matters: If you deploy foundation models, GPAI rules apply. Understanding the classification helps navigate compliance requirements.
H
HIPAA (Health Insurance Portability and Accountability Act)
US federal law protecting health information (PHI). Requires administrative, physical, and technical safeguards for covered entities and business associates handling medical data.
Why it matters: Healthcare AI must operate within HIPAA boundaries. On-premise deployment and synthetic data can enable use cases that cloud AI cannot touch.
I
IRB (Institutional Review Board)
Ethics committee that approves research involving human subjects. In healthcare AI context, reviews data usage, privacy controls, and consent mechanisms.
Why it matters: AI training on clinical data requires IRB approval. Documented privacy controls and synthetic data options can accelerate this process.
K
KB (Knowledge Base)
Structured repository of organizational documents, policies, and reference materials that AI retrieves from when answering queries. Indexed for both keyword and semantic search.
Why it matters: AI is only as good as its knowledge source. A well-organized KB with proper access controls enables accurate, grounded responses.
L
LIME (Local Interpretable Model-agnostic Explanations)
Explainability technique that approximates any model's behavior locally around a specific prediction using interpretable models. Shows which features drove a particular decision.
Why it matters: When stakeholders ask "why did AI decide this?", LIME provides human-readable explanations - critical for regulated decisions.
LLM (Large Language Model)
AI model trained on vast text data to understand and generate human language. Examples: GPT-4, Claude, Llama, Mistral. The "engine" powering conversational AI and text generation.
Why it matters: Understanding what LLMs can and cannot do helps set realistic expectations - they're powerful but need guardrails for enterprise use.
M
MARM REALM
Memory-Anchor-Retrieval-Management. AYITA's core memory architecture providing persistent, semantically typed context with governance policies, lifecycle management, and deterministic behavior.
Why it matters: Enterprise AI needs memory that's inspectable, controllable, and auditable - not a black box that "just remembers things."
MRM (Model Risk Management)
Framework for governing AI/ML models in financial services - covering validation, monitoring, documentation, and lifecycle management. Required by SR 11-7 regulatory guidance.
Why it matters: This team validates every model before production. Having model passport documentation and audit trails ready accelerates their review.
Model Passport REALM
Governance record for each AI model capturing origin, capabilities, training data lineage, intended use cases, and compliance status. Part of Model Portfolio architecture enabling role-based model selection.
Why it matters: When auditors ask "what model did this?", the passport provides complete provenance - not "we used GPT-something."
O
Orchestrator REALM
Deterministic control layer that manages AI operations: routing queries, enforcing policies, coordinating memory access, and ensuring LLMs execute within governance boundaries. "Rules manage, LLM executes."
Why it matters: AI without orchestration is unpredictable. The orchestrator ensures consistent, auditable behavior regardless of which model generates the response.
P
PAMOLA REALM
Privacy Engineering Studio. Platform for transforming sensitive data with measurable privacy guarantees: synthetic data generation, differential privacy profiles, and audit-ready evidence artifacts.
Why it matters: Enables AI development on sensitive data without exposing originals - with mathematical privacy guarantees reviewers can verify.
PHI (Protected Health Information)
Any health data that can identify an individual - including medical records, lab results, insurance claims. Regulated by HIPAA in US with specific handling, storage, and disclosure requirements.
Why it matters: AI systems processing PHI face strict requirements. Synthetic data or on-premise deployment can enable use cases that cloud AI cannot touch.
Privacy Score REALM
Measurable risk indicator (0-1 scale) quantifying disclosure risk for data or AI outputs. Used for governance decisions, not compliance certification.
Why it matters: Transforms privacy from subjective judgment to measurable metric - enabling consistent policy enforcement and audit documentation.
PII (Personally Identifiable Information)
Any data that can identify an individual - names, emails, SSNs, IP addresses, biometrics. Broader than PHI; applies across industries and jurisdictions.
Why it matters: PII detection and handling is foundational for AI compliance. Systems must identify, protect, or redact PII before processing.
PATE-GAN
Private Aggregation of Teacher Ensembles for GANs. Technique combining differential privacy with generative models to create synthetic data with formal privacy guarantees.
Why it matters: One of several privacy-preserving synthetic data methods. Understanding options helps choose the right approach for your data type and privacy requirements.
R
RAG (Retrieval-Augmented Generation)
Pattern where AI retrieves relevant context from a knowledge base before generating responses. Grounds outputs in actual documents rather than relying solely on model training.
Why it matters: RAG reduces hallucination and enables AI to work with current, organization-specific information - not just what was in training data.
S
SOC 2
Service Organization Control Type 2. Audit standard examining security, availability, processing integrity, confidentiality, and privacy controls over a period (typically 12 months).
Why it matters: Standard checkbox in enterprise vendor assessments. Having SOC 2 evidence ready (or clear roadmap) removes a common procurement blocker.
Synthetic Data
AI-generated data that preserves statistical properties of original dataset while containing no real individuals. Enables analytics, testing, and model training without privacy risk.
Why it matters: Unlocks data sharing and AI development that's blocked by privacy regulations - with mathematically provable protection.
SHAP (SHapley Additive exPlanations)
Explainability method using game theory (Shapley values) to quantify each feature's contribution to a model's prediction. Provides consistent, theoretically grounded feature attribution.
Why it matters: Goes beyond "which features mattered" to "how much each feature pushed the prediction" - the level of detail MRM teams expect.
T
TPRM (Third-Party Risk Management)
Procurement-led process for assessing and monitoring risks from vendors, partners, and external service providers handling sensitive data or critical operations.
Why it matters: This team often blocks AI deployments until evidence is review-ready. Pre-built evidence packs directly address their standard questionnaires.
X
XAI (Explainable AI)
Field of AI focused on making model decisions understandable to humans. Includes techniques like SHAP, LIME, attention visualization, and counterfactual explanations.
Why it matters: Regulators and risk teams increasingly require AI explainability. XAI techniques turn "the model said so" into "here's why the model said so."
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