OAuth 2.0 is an open standard protocol that allows third-party applications, like a website or application to access the resources of a user without exposing their credentials. For instance, it allows apps to access your data without giving them your password, keeping your information secure.

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.

Adversarial inputs are carefully designed changes to data that confuse AI models and cause them to make mistakes. These changes can be subtle and hard for humans to detect, but they exploit weaknesses in the AI’s understanding. Adversarial inputs can reduce the accuracy and reliability of AI, so defending against them is important to ensure AI makes correct and safe decisions.

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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.

AgentControl provides fine-grained, context-aware authorization for AI agents, ensuring they access only the data and systems required for their specific tasks. By dynamically evaluating permissions in real time, it prevents over-permissioned agents from exposing sensitive information or violating policies.

Why it matters: Proper agent access ensures AI agents can perform tasks safely and efficiently, while reducing security risks, preventing unauthorized data use, and maintaining compliance.

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 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.

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.

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.