Access control is a crucial aspect of security that defines who can access specific data, applications, and resources - as well as under what conditions. In short, it's a selective restriction of access to a resource. Access control heavily depends on authentication and authorization techniques, enabling organizations to verify users’ identities and ensure they receive the correct level of access based on the authorization policy.

Active metadata refers to dynamic information about data that is constantly updated and used to manage and optimize how data is organized, accessed, and used in different systems. It goes beyond metadata with only static descriptions and helps keep track of data's properties, relationships, and usage patterns in real-time, which is crucial for ensuring data quality, compliance, and efficient data management. Essentially, active metadata enables systems to automatically adapt and improve based on the most current information about the data.

Adaptive access refers to a security approach that adjusts access permissions based on real-time user behavior, contextual factors and risk assessments. Just picture a system that changes its rules depending on where you are, what device you’re using and how you normally behave online, and then decide to grant access or not.

Why it matters: Adjusting access in real time based on context reduces security risks while maintaining user productivity.

Agent access refers to the permissions and rights granted to an AI agent to interact with systems, data, or resources in order to perform its tasks. Access is typically controlled through policies, authentication, and authorization mechanisms.

Why it matters: Proper agent access ensures AI agents can perform their functions safely while preventing unintended or malicious use of sensitive data.

Agentic AI identity security ensures that AI agents have verified, managed, and auditable identities, with controlled access to resources and data.

Why it matters: Proper identity security prevents misuse, impersonation, or unauthorized actions by AI agents.

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Agentic AI security involves protecting autonomous AI systems by managing the unique risks that arise from their independent decision-making and actions. This includes preventing misuse, controlling unintended behaviors, ensuring robustness against attacks, and maintaining trustworthiness as these AI agents operate alone or collaboratively.

To learn more, download the E-guide: Access Control for AI Agents.

Agentic AI refers to AI systems designed to operate autonomously, making decisions and performing tasks independently by mimicking human-like reasoning and initiative. These systems are made up of AI agents that may act alone or coordinate with others to achieve shared or individual goals.

Agentic AI refers to AI systems designed to operate autonomously, making decisions and performing tasks independently to achieve goals, often interacting with other agents or humans.

Why it matters: Agentic AI enables scalable automation and complex decision-making, but requires careful governance to ensure safe, ethical, and compliant behavior.

Agentic commerce is a specific application of agentic AI in shopping, payments, and commerce, where autonomous AI agents act on behalf of users to complete tasks such as finding products, comparing options, and making purchases. Unlike general agentic AI, which can operate across multiple domains like customer service, software development, or content creation, agentic commerce focuses exclusively on commercial and retail activities, using context, memory, and tools to make autonomous decisions.

Why it matters: Agentic commerce enables faster, more personalized, and efficient shopping experiences, while requiring clear permissions and governance to ensure trust, accountability, and safe use.

Agentic security is the overarching practice of securing autonomous AI agents and the systems they operate within. It overlaps with AI agent security but is broader, addressing risks that emerge when agents coordinate or operate collectively, while also managing lifecycle-level protections. This includes governing access, enforcing policies, and monitoring autonomous actions across the ecosystem.

Why it matters: Without agentic security, autonomous agents could be manipulated, leak sensitive data, or make unsafe decisions, creating cascading failures or systemic vulnerabilities across multi-agent environments.

An AI agent is an autonomous software system that perceives its environment, makes decisions, and acts to achieve specific goals on behalf of users, often without human intervention. AI agents can reason, plan, remember, learn, and adapt, enabling them to complete complex tasks with a degree of independence.

Why it matters: AI agents unlock automation and efficiency for complex workflows, but require careful governance and oversight to ensure safe, ethical, and compliant operation.

An AI context engine is a system that gathers, interprets, and manages contextual information - such as user intent, data sensitivity, environment, and task relevance - to guide AI behavior and decision-making. It integrates diverse data sources, connects related information, and builds a unified understanding that allows AI models and agents to act appropriately within specific circumstances. Unlike basic AI systems that follow predefined rules, a context engine provides a dynamic intelligence layer that enables real-time, context-aware reasoning and more accurate, relevant responses.

‍Why it matters: By supplying continuous contextual intelligence, an AI context engine ensures AI outputs are precise, secure, and aligned with business objectives and compliance requirements, strengthening both performance and trust in AI systems.

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 AI-ready data model is data that has been systematically prepared, structured, and governed so that AI and machine learning systems can use it accurately and efficiently. It is clean, complete, and contextualized, with clear metadata, allowing AI systems to easily find, process, and learn from it to make reliable predictions and decisions.

Why it matters: AI-ready data models ensure AI systems can operate effectively, reduce errors, and deliver scalable, reliable outcomes.

An agent-native data model is a way of structuring data specifically for AI agents to use efficiently. It emphasizes relationships, context, and attributes that agents need to make autonomous decisions.

Why it matters: Structuring data in an agent-native way ensures agents have the right information at the right time, improving accuracy and reducing errors.

An agentic application is software that incorporates one or more AI agents to execute tasks, respond to conditions, or make decisions autonomously.

Why it matters: Agentic applications enhance productivity and responsiveness, but must have clear policies to prevent unintended outcomes.

An agentic experience refers to interactions where AI agents actively assist or complete tasks for users, providing guidance, recommendations, or actions autonomously. These experiences showcase the potential of agent-native systems to streamline workflows or enhance user-facing services.

Why it matters: Agentic experiences can improve efficiency, personalization, and user satisfaction, but require careful design, transparency, and control to ensure trust and safe adoption.

An agentic security risk is the potential harm that arises from the autonomous actions of AI agents, including unauthorized access, data misuse, malicious exploitation, or unintended behavior caused by errors or misconfigurations. These risks emerge from the agent’s ability to act independently, interact with systems, and access sensitive resources.

Why it matters: Understanding agentic risks enables organizations to implement proper safeguards, prevent misuse, and ensure agents operate safely and responsibly.

An agentic security solution is a framework, platform, or set of controls designed to protect autonomous AI agents and the environments they operate in. These solutions can govern agent permissions, monitor decision-making, enforce policy boundaries, detect malicious behavior, and prevent misuse of tools or data during autonomous actions.

Why it matters: As AI agents gain more autonomy and system access, dedicated security solutions are critical to ensure safe operation, prevent exploitation, and maintain trust in agent-driven workflows.

An agentic security threat is a malicious or accidental event that targets or exploits autonomous AI agents and their ability to act independently. Threats may involve manipulating an agent’s decisions, misusing its tools, triggering unauthorized actions, or causing data leakage or system disruption. Examples include memory poisoning, policy circumvention, or hijacking agent workflows.

Why it matters: Recognizing agent-specific threats is essential for designing secure, resilient AI systems that prevent autonomous agents from being weaponized or acting unpredictably.

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.

Discover IndyKite Identity Knowledge graph

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.

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.

Learn more 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.