Just in time access (JIT) refers to a process of temporarily granting on-demand (privileged) access only when needed for a specific task or period. Access is provided dynamically and automatically based on predefined policies and conditions. It’s like asking and getting a temporary key to a room only when you need to go inside. You don't have permanent access, but you can enter when necessary.

Why it matters: Temporary, on-demand access minimizes exposure, reduces the attack surface, and supports compliance.

Knowledge-based Access Control (KBAC) leverages contextual and relational data to drive granular authorization decisions. At the core of the IndyKite Identity Platform is the Identity Knowledge Graph, which gathers data from various sources to create an operational data layer. To manage access, KBAC is added, using connected and enriched data to make real-time, context-aware authorization decisions based on your business needs.

Discover our Introduction to Knowledge-based Access Control.

Learn more about KBAC here.

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A knowledge graph, also known as a semantic network or connected data model, represents a network of real-world entities, made up of nodes, edges and labels, and illustrates the relationships between them - visualized as a graph structure. Imagine a smart map that connects pieces of information together, and shows how things are related. By doing so, we can find unique connections and new insights, which makes it easier to answer complex questions, and provide helpful recommendations.

Why it matters: Mapping relationships between entities uncovers hidden insights, improves recommendations, and supports complex queries.

Least privilege is a security concept that restricts user access rights to the minimum level needed to perform the job, based on roles and responsibilities. Benefits include; enhanced data security, mitigated risk associated with unauthorized access, and ensured compliance with regulatory standards for data protection.

LLM security involves safeguarding large language models and their related systems against risks like data leaks, prompt injection attacks, misuse, and unauthorized access. It involves securing the data used to train and interact with the model, as well as the model’s behavior and outputs.

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MCP (Model Context Protocol) allows for seamless integration and communication between AI models and different components, such as tools, data sources, and services. By standardizing how context and capabilities are shared, MCP enables AI to access relevant information, interact with external systems, and perform tasks more effectively and securely.

Why it matters: MCP enables agents to operate more securely and consistently by standardizing how they access tools, data, and capabilities.

Model inversion is an attack method targeting AI models, where an attacker infers information about the model's training data by analyzing the model's output. It effectively “reverse-engineers” the model to uncover the data it was trained on, which can lead to exposure of sensitive or private information.

‍Why it matters: Model inversion can expose private or proprietary data, posing serious risks to privacy, security, and compliance.

Multi-agent security covers the protection and governance of environments where multiple AI agents interact, collaborate, or compete. It addresses risks that emerge from agent-to-agent coordination, shared tools, and cascading decision chains.

Why it matters: Coordinated agents can create expanded attack surfaces or compounding errors, so multi-agent security is essential to prevent unintended behaviors, exploitation, and systemic failures.

Learn how IndyKite provides multi agent security here.

A multi-agent system is a group of AI agents that interact, collaborate, or compete to achieve individual or collective goals. Such systems often require coordination and communication protocols.

Why it matters: Multi-agent systems enable complex problem-solving and automation at scale, but introduce additional governance, security, and coordination challenges.

An ontology is a shared, formal definition of an organization’s core business concepts and the relationships between them. It provides a structured way to represent meaning so that systems can understand not just data, but what that data represents in the real world.

Why it matters:
Ontologies give AI systems clear concepts to reason about instead of inferring meaning implicitly. This enables more accurate reasoning, better interoperability, and reduced ambiguity across models, agents, and applications.

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.

OWASP agentic security is an initiative by the Open Web Application Security Project that provides security guidelines, threat models, and best practices for protecting autonomous AI agents and agentic systems. It focuses on mitigating risks such as unauthorized access, tool misuse, memory poisoning, data leakage, and unsafe autonomous behavior across the agent lifecycle.

Why it matters: Adopting OWASP-aligned principles helps organizations identify and reduce common vulnerabilities in agentic AI, ensuring safer, more trustworthy autonomous systems.