AI & Data Glossary

An iterative space for an AI agent to document its thoughts, tool calls, and observations before taking a final action.

A central "supervisor" LLM breaks down a user request and delegates sub-tasks to specialized "worker" agents.

A decentralized pattern where independent agents communicate directly with each other to solve a task, without a central supervisor.

A constraint that limits the maximum number of reasoning cycles an agent can take to prevent infinite loops.

Prompting an AI to critique and refine its own draft output before presenting it to the user.

The ability of a cognitive agent to break a complex, open-ended goal into smaller, executable steps.

Executing multiple independent tasks simultaneously across different agents (fan-out) and aggregating the results (fan-in) to save time.

Summarizing or compressing older conversation history to prevent the LLM from crashing due to memory (token) limits.

When an LLM hallucinates a tool that doesn't exist or passes the wrong API parameters to a real tool.

Monitoring how the output of one agent cascades and influences the decisions of downstream agents.

ISO 42001 is a strict, certifiable management standard. NIST AI RMF is a flexible, voluntary framework for managing AI risk.

A continuous improvement cycle for managing systems (the core of ISO standards).

The principle of collecting only the data strictly necessary for a system to function.

A strict security boundary that prevents even authorized users from moving data outside a trusted network (Privileged Exfiltration).

Testing security policies by monitoring what would be blocked, without actually blocking live traffic.

Ensuring autonomous agents maintain immutable, step-by-step logs of their reasoning and actions.

A standard token containing verifiable assertions about a user's identity and authentication status.

A multi-layered data structure: Bronze (Raw landing zone), Silver (Cleaned/Validated), Gold (Business-ready/Aggregated).

An architecture combining the cheap storage of a data lake with the fast, structured querying of a data warehouse.

Decentralizing data ownership so specific business units manage their own data as a "product" rather than a central IT team.

RBAC grants access based on static roles (job titles). ABAC evaluates dynamic attributes (user, environment, time, device).

Decoupling authorization logic from app code into a central, readable policy engine.

Never trusting any entity by default; requiring continuous verification regardless of network location.

Obscuring sensitive data in real-time when queried by unauthorized users, without changing the database.

Tracking the flow and transformation of data from its origin to its final destination.

How an institution decides who can approve AI use, what controls must be in place, how risk is reviewed, and who is accountable when something goes wrong.

A leadership discipline that decides where AI can create real value, which use cases matter most, what capabilities must be built, and what guardrails must be in place.

Identifying where an AI use case may create harm, bias, security exposure, privacy risk, operational failure, or reputational damage — before deployment.

A credible model that combines financial benefit with operational value, risk reduction, and service improvement — not cost savings alone.

A design choice about where people review, approve, override, or escalate AI outputs before harm occurs.

Focusing on high-friction processes, repetitive work, service bottlenecks, decision delays, and information overload — where AI can deliver visible business value.

Defines how strategy, governance, delivery, risk, security, and business ownership work together across the AI lifecycle.

A metric that balances precision and recall; highly reliable for imbalanced datasets where simple accuracy is misleading.

The time it takes for the fastest 95% of requests to complete; highlights the "tail" or slowest user experiences.

Data Drift: Input data changes. Concept Drift: The relationship between input and output changes (rules of the game change).

Using statistical thresholds for User Acceptance Testing because LLMs rarely produce the exact same output twice.

The risk where tweaking a master prompt to fix one feature inadvertently breaks another feature relying on the same prompt.

An adversarial attack where malicious commands are hidden in external data that an agent autonomously retrieves.

A UI pattern showing how certain the AI is about its output to prevent users from blindly trusting hallucinations.

Exposing the factors and reasoning that led an AI to a specific decision.

A design pattern that surfaces simple information first, revealing complex AI reasoning or citations only when requested.

The degradation of an AI model's performance in production as the real world changes and diverges from the training data.