An AI Security Platform (AISP) is a centralized system designed to protect both third-party and custom-built AI applications. It combines AI usage control and AI application cybersecurity to monitor, enforce policies, prevent data leaks, and secure AI systems across the organization.

‍Why it matters: AISPs reduce AI-native security risks, provide unified visibility, and simplify governance for enterprise AI adoption.

An identity knowledge graph, is a real-world network of both person and non-person entities and the relationship between them. The graph captures all identifiers related to an entity, including dynamic attributes such as location, and stores this for each data node, along with capturing what the relationship is between entities which provides ‘context’. An identity knowledge graph can be used to unify data across an organization, applications and channels. The end result is a holistic, connected view of your customers, partners, entities that you can leverage for analytics, AI, access and insights.

Why it matters: A unified, contextual view of identities improves decision-making, personalization, and secure access control.

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An operational data layer is there to support a business with their operations. Operationalizing means that you are putting your data into operation, versus just doing data tasks and not making use of them. An operational data layer means that it is an intelligent and well structured layer to move data into the organization to deliver outcomes. It aggregates and integrates data from multiple sources, providing a unified, current view of the data necessary for day-to-day business functions. Hence, it is both the infrastructure and the tooling to deliver data to the organization.

Why it matters: Operational data layers turn raw data into actionable intelligence, enabling real-time decisions and business outcomes.

A schema is the formal definition of what data exists within a system and how it is structured, including data types, constraints, and relationships. It sets expectations for how data should be stored, validated, and consumed.

Why it matters:
Schemas provide consistency and predictability, allowing AI systems to know what data to expect and how to handle it. This structure reduces errors, supports governance, and enables reliable integration across systems.

A semantic layer is a business-friendly abstraction layer that defines shared concepts, metrics, and definitions on top of raw data. It translates technical data structures into consistent, human and machine understandable meaning, ensuring that both people and AI systems interpret numbers and metrics the same way.

Why it matters:
By establishing agreed definitions and metrics, a semantic layer prevents misinterpretation and disagreement over what data represents. This consistency is essential for AI systems, which rely on clear meaning to reason accurately and deliver trustworthy results at scale.

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.

Attribute Based Access Control (ABAC) is a security approach that uses attributes (such as title, location, team, etc) to determine access to a resource. A system administrator would be the one to set approved characteristics to determine access. 

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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.

Authorization, or AuthZ, is a critical enabler of most systems, be that workforce environments or consumer applications. Based on a set of policies, it determines what actions users are permitted to perform and what resources they can access. Modern approaches use authorization as a key driver of personalized experiences, ensuring efficient and secure access tailored to each user’s role and context.

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AuthZen (Authorization Enhancement) is a standard by the OpenID Foundation that defines an interoperable protocol for fine-grained authorization. It enables Policy Enforcement Points (PEPs) and Policy Decision Points (PDPs) to work together using rich contextual data to make precise access decisions.

‍Why it matters: AuthZen helps organizations enforce precise, policy-driven access decisions, improving control, compliance, and interoperability.

B2B data sharing involves securely sharing or accessing data from one entity to another for business purposes. For example to enable collaboration, improve services or simply create mutual value. This often involves sharing customer insights, supply chain data or analytics, while ensuring privacy, security and compliance with regulations.

Why it matters: Secure, well governed data sharing drives collaboration, innovation, and value creation without exposing sensitive information.

Learn more about B2B data sharing here.

Business data refers to any data that is related to a business; its operations, performance, activities, etc. A financial service company could for example use business data to manage client portfolios; from analyzing client data to tailored investment strategies, using market data to mitigate risks, and enhance client interactions through detailed transaction history.

Connected data refers to data stored on a graph data model, which enables relationships between data points. Graph has a unique ability to understand dynamic and complex relationships and manages data in a more natural, intuitive way, giving context to otherwise meaningless information. Connected data is a powerful force, offering greater flexibility, insight and speed for data driven projects.

Why it matters: Connected data enables richer analysis, more precise authorization, and faster response to business or security needs.

Context-aware enforcement is a type of dynamic enforcement that applies access and security policies specifically based on real-time contextual information, such as user identity, device status, location, and behavior. By evaluating the circumstances surrounding each request, it ensures that access or actions are only allowed when appropriate.

Why it matters: By using contextual intelligence, organizations can prevent unauthorized actions while enabling legitimate system use, enhancing security without disrupting productivity.

Context-aware security is the practice of using situational information to enhance security decisions in real time. It takes into account factors like user location, device type, time of access, and network conditions to dynamically adjust access controls and security measures. This approach enables more adaptive and precise protection - reducing the risk of threats while still allowing legitimate users to access what they need.

‍Why it matters: Adaptive, context-based security reduces threats more precisely than static rules, strengthening protection without hindering productivity.

Context-based access control (CBAC) is a dynamic security model that makes adaptive, risk-aware access decisions by evaluating multiple real-time situational factors, such as user behavior, device health, location, and network conditions, instead of relying solely on static rules.

Why it matters: CBAC enhances security while maintaining operational efficiency, ensuring sensitive resources are accessed safely and appropriately.

Contextual access control is a dynamic security approach that grants or denies access based on real-time contextual factors, rather than just static attributes like user identity. It evaluates variables such as role, location, device, time, and activity to assess the risk of each access request.

Why it matters: Considering the circumstances of each request reduces the risk of unauthorized access while allowing legitimate users to operate efficiently, supporting both security and usability.

Contextualized data refers to information that is enhanced with relevant context, such as time, location, environmental conditions, historical trends, or external events to provide deeper insights and greater understanding. Traditional databases can’t capture context, however connected data models can in the form of relationships to other data points, attributes and metadata. Contextualized data provides a richer view that can enhance workflows for identity and access management, threat detection, predictive models and personalization.

ContX IQ is a IndyKite product that combines data retrieval and enforcement to t enable secure, real-time delivery of data to the right place in the right context. It allows organizations to define business parameters, run contextual queries, and fetch data (without duplication) tailored to specific situations, while simplifying integrations and maintaining access control and consent management.

Why it matters: ContX IQ ensures that data is shared safely, efficiently, and in alignment with policies, reducing engineering overhead and supporting trust in AI-driven processes.

Data access refers to a user's ability (with permission granted) to retrieve, manipulate, or interact with data stored in a system or database. Simplified, it’s like having a key to unlock a safe where information is stored, allowing you to view, change, or use the data based on your permissions.

Data poisoning, also known as AI poisoning, involves a deliberate and malicious contamination of data to compromise the performance of AI and ML systems. Attackers may inject false, misleading, or manipulated data into the training process to degrade model accuracy, introduce biases, or cause targeted misbehavior in specific scenarios.

Why it matters: Poisoned data can corrupt models, degrade performance, or lead to manipulated outcomes, undermining reliability and safety.

Data assurance is the process of validating that data is accurate, complete, governed, and appropriate for use in applications, analytics, or AI systems. It includes evaluating provenance, quality, consistency, and usage permissions.

‍Why it matters: AI and decision systems are only as reliable as the data they rely on, and assured data reduces the likelihood of incorrect, biased, or non-compliant outcomes.

Data classification involves organizing data into categories to enhance its usability and security. This process simplifies data retrieval and is crucial for risk management, compliance, and data security efforts.

Data enablement is the means of empowering an organization to collect the full potential of their data. It involves ensuring that data is properly integrated, managed, and delivered to the right users in a meaningful way, so it can be used effectively to drive decision-making and innovation.

Why it matters: Effective data enablement allows organizations to leverage their full data potential for innovation and growth.

Data entity matching refers to the task to figure out if two entity descriptions actually refer to the same real-world entity. By identifying, linking and merging similar or identical entities across different datasets you can create a unified and accurate representation. The goal is to build a cohesive dataset, enabling clearer insights and more informed decision-making.