A system of intelligence is an advanced, AI-driven software architecture that continuously ingests signals, builds context, and applies decisions across data, users, and systems, enabling automated workflows and actionable, real-time insights as conditions change.

Technical debt refers to the total expense caused by inadequate architecture or software development. These may be decisions to prioritize speed over design, but it is more often the result of short-sighted, siloed software decisions without a view to the broader architecture. Legacy solutions that have become obsolete over time, but are incorporated in a way that is difficult to remove also contributes to an organization’s technical debt.

AI Trust, Risk, and Security Management (TRiSM) is a framework for overseeing the safety, reliability, and compliance of AI systems across their lifecycle. It provides governance, monitoring, and controls to manage AI risks, including bias, misuse, and operational vulnerabilities.

Why it matters: Implementing TRiSM ensures organizations deploy AI responsibly, reduce operational risk, and maintain trust in AI-driven decisions.

Trusted data use is the practice of using data with confidence that it is accurate, reliable, complete, and governed. It ensures that data meets defined quality, security, and compliance standards, allowing it to be used safely across systems, analytics, and AI.

Why it matters: Trusted data use enables consistent, compliant, and confident decisions, ensuring that data-driven systems operate safely and effectively.

Trust fabric is a concept primarily driven by Microsoft, defined as a real-time approach to securing access that is adaptive and comprehensive. A trust fabric authenticates identities, verifies access conditions, checks permissions, encrypts the communication channel, and monitors for security breaches. All continuously evaluated in real-time.

A method for evaluating the reliability of data based on two aspects, Data Integrity and Data Provenance. Data Integrity measures quality through dimensions such as freshness (how up to date the data is), completeness (whether all expected properties are present), and validity (accuracy of representation). Data Provenance tracks lineage and transparency through origin (where the data came from) and verification (when it was last authenticated). Together, these provide a configurable score indicating overall trustworthiness. The goal of trust scoring is to provide a measurable level of confidence in the data, allowing organizations to make more informed decisions by using data that has been verified as trustworthy.

‍Why it matters: Trust scoring strengthens governance and accountability by providing a measurable way to assess data reliability before it is used in critical processes or AI systems.

Unauthorized inference refers to the act of gaining access to sensitive information by exploiting an AI system’s responses or behavior, without direct access to the underlying data. It takes advantage of the possibility that public information may reveal hints about concealed data or system behaviors.

‍Why it matters: Preventing unauthorized inference protects sensitive information from being indirectly exposed through model outputs.

Unified data refers to the alignment of data from various sources into a single, coherent view, enabling comprehensive analysis and decision-making. Unifying data across the organization provides an opportunity to remove both technical and organizational silos, gain greater understanding and insight and enable better business outcomes.

A unified data layer, also known as connected data, refers to data stored in a graph data model, which captures relationships between data points. This approach excels at understanding dynamic and complex relationships, managing data intuitively, and providing context to otherwise meaningless information. Connected data offers greater flexibility, insight, and speed for data-driven projects, making it a powerful force in the data management landscape.

Why it matters: Centralized, connected data ensures consistency, improves efficiency, and reduces errors across systems and processes.

Unstructured data is information that does not follow a predefined schema or table format, which makes it difficult to organize, query, or govern using traditional database tools. It exists in many forms, including documents such as contracts and manuals, emails and chat messages, and rich media like images and videos. This content is often spread across file systems, cloud drives, and collaboration platforms, usually without consistent tagging or governance.

Why it matters:
Unstructured data holds most of an organization’s meaningful context and institutional knowledge, which makes it essential for AI systems that rely on language, reasoning, and interpretation. As LLM driven applications become standard, unstructured content shifts from a loosely managed resource to a critical input for accurate, secure, and scalable AI.

User-approved data sharing essentially means giving permission to a third party to use your personal data. This practice ensures that users have control over which data is shared, with whom, and for what purpose.

Zero-party data is information explicitly provided by the customer, usually through direct interaction or surveys, unlike other forms of data, which are typically observed or inferred. It’s like going shopping and telling the store exactly what you are looking for, and by sharing your preferences directly, they can give you personalized recommendations and offers based on what you have told them.