AI data governance is an extension of traditional governance that focuses on managing the unique risks and complexities of AI systems. It ensures that data feeding AI systems is visible, well-understood, and governed by context-aware metadata—capturing provenance, usage constraints, and trust signals. This enables enterprises to safely scale AI, enforce data policies at the point of use, and ensure decisions are grounded in reliable information.

Learn more about AI data governance in our Knowledge Center.

AI security threats are risks and vulnerabilities that target or arise from AI systems, potentially compromising their integrity, confidentiality, or availability. Such threats can include adversarial attacks, data poisoning, model inversion, unauthorized inference, and misuse of AI for malicious purposes.

Read more about these risks and how to mitigate them in the Knowledge Center.

Policy Decision Points are parts of a system that review access requests against set rules and available context, then decide whether to approve or deny the request, sending that decision back to a Policy Enforcement Point (PEP).

‍Why it matters: PDPs centralize decision logic, allowing for scalable governance and consistent policy enforcement across systems.

Policy Enforcement Points are parts of a system that control access by checking each request, asking a Policy Decision Point (PDP) for a decision, and then allowing or blocking the request based on that decision.

‍Why it matters: PEPs ensure access is consistently enforced in real time, preventing unauthorized actions before they occur.

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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Data silos refer to isolated collections of data, such as customer or sales data, within an organization that are not easily accessible or integrated with other data sources. Imagine having separate storage rooms for each department, where each room holds important information, but each department can only access their own storage room. This makes it difficult to get a complete unified view of the entire organization’s data.

A directory information service is a centralized database which stores, manages, and provides access to directory data, such as user identities, resources, and access permissions. Picture a company’s phonebook, listing all employees, their contact information, and their roles, helping everyone find the right person quickly.

Dynamic access tokens are temporary, context-aware credentials that grant AI agents or users secure access to resources. They can adjust permissions in real-time based on policies, risk, or environmental factors.

Why it matters: Dynamic tokens improve security by limiting exposure and ensuring access is only granted under the right conditions.

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.

Agent-native refers to systems and workflows intentionally designed for autonomous AI agents to operate at the core, instead of being added on top of workflows designed for humans. It reflects a shift toward environments where agents execute tasks, make decisions, and interact with other systems independently, while humans guide overall direction.

Why it matters: Agent-native design ensures agents can operate safely and efficiently, enabling greater automation, scalability, and alignment with business goals.

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.

AI agent security focuses on protecting individual autonomous AI agents and their interactions with data, systems, and users. It governs the agent’s access, enforces policies, and monitors its actions to prevent misuse, exploitation, or unintended harm.

Why it matters: Securing individual AI agents protects data integrity, privacy, and operational reliability, ensuring the agent behaves safely and as intended.

AI assurance is the ability to verify, monitor, and demonstrate that AI systems operate safely, comply with governance or regulatory requirements, and can be audited. It includes traceability of data inputs, model decisions, and system actions.

‍Why it matters: Assurance is necessary to meet internal risk controls, satisfy legal standards, and maintain accountability for AI-driven outcomes.

AI data security involves making sure the data used by AI systems is reliable, properly managed, and safeguarded from abuse, while also maintaining transparency and trust at every stage. This solid framework allows organizations to deploy AI with confidence, meet regulatory requirements, and protect sensitive data.

AI governance is the control framework that governs how data is accessed, interpreted, and used by AI systems. It ensures that AI operates within defined boundaries—enforcing data use restrictions, applying contextual metadata, and maintaining traceability—so enterprises can deploy AI safely, compliantly, and at scale.

Learn more about AI governance in our Knowledge Centre.

AI orchestration is the process of managing and coordinating multiple AI agents, models, tools, and data sources so they work together seamlessly and efficiently. It ensures that AI components interact correctly, share data appropriately, and respond dynamically to changes in context or environment.

Why it matters: AI orchestration enables organizations to scale autonomous systems safely, optimize performance, and maintain control and compliance across complex AI-driven processes.

AI poisoning, also known as data 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.

Learn more about how to protect against AI poisoning here.

Prompt injection is when someone inserts harmful or misleading text into an AI’s input to manipulate how it responds. This can cause the AI to produce incorrect, biased, or even dangerous outputs, or reveal information it shouldn’t. Because prompt injection can make AI behave in unexpected or harmful ways, protecting against it is key to keeping AI systems safe and trustworthy.

Why it matters: Prompt injection can cause AI to produce harmful or unauthorized outputs, making it a critical threat to trust, security, and brand safety.

AI risk mitigation is the process of identifying, assessing, and reducing the potential threats associated with the development and use of AI systems. It involves proactively managing risks - such as bias, security vulnerabilities, privacy concerns, and unintended behaviors - to ensure AI systems are safe, ethical, reliable, and compliant with regulations.

‍Why it matters: Proactively reducing AI risks supports safer deployment, regulatory compliance, and long-term trust in AI systems.

AI risk refers to the potential harm or exposure arising from AI systems that are insecure, biased, poorly governed, or used outside their intended context. This can include data leakage, ethical issues, compliance violations, manipulation attacks, or unsafe decision-making.

‍Why it matters: Unmanaged AI risk can result in reputational damage, financial loss, operational disruption, and regulatory penalties.

AI trust is the confidence that AI systems behave reliably, securely, and in alignment with intended goals and constraints. It involves assurance that outputs are explainable, fair, accountable, and consistent over time.

‍Why it matters: Without trust in AI decisions, organizations cannot safely scale adoption or meet stakeholder, customer, and regulatory expectations.

AI-ready data is data that is accessible, trustworthy, enriched, and of high quality, ensuring accuracy and relevance for AI applications. In essence, it is information specifically prepared and optimized for use in artificial intelligence and machine learning models. This is crucial because the quality and preparedness of data directly impact the effectiveness, reliability, and fairness of AI systems.

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AI prompt injection is when someone inserts harmful or misleading text into an AI’s input to manipulate how it responds. This can cause the AI to produce incorrect, biased, or even dangerous outputs, or reveal information it shouldn’t. Because prompt injection can make AI behave in unexpected or harmful ways, protecting against it is key to keeping AI systems safe and trustworthy.
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Learn more about how to protect your AI systems here.

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. 

Learn more here.

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.

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.

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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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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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.

Policy Based Access Control (PBAC) is authorization approach that organizes access privileges based on a user’s role (predefined rules or policies) to determine who is granted access to resources and under what conditions. Policies can consist of a variety of attributes, such as: name, organization, job title, security clearance, creation date, file type, location, time of day and sensitivity or threat level. Once these are combined to form policies, rules are established to evaluate who is requesting access, what they are requesting access to and the action determining access.

Learn more here.

RAG protection refers to securing Retrieval-Augmented Generation (RAG) systems by controlling how data is accessed and used. It includes fine-grained authorization to make sure only the right information is shared in the right context, helping prevent data leaks or unauthorized access during AI interactions.

‍Why it matters: RAG systems often access sensitive information, so proper protection prevents data leakage, ensures compliance, and maintains trust in AI-driven responses.

RAG security focuses on protecting Retrieval-Augmented Generation (RAG) systems by using technical measures to secure data, prevent unauthorized access, and maintain privacy. It ensures the system and data are safe from misuse.

Learn more by downloading the E-guide: RAG Security.

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Retrieval-Augmented Generation (RAG) is a method in AI that combines two steps: first, it finds (retrieves) useful information from sources like databases or documents; then, it uses an AI model to create a response based on that information. This makes the AI’s answers more accurate and helpful, especially for tasks like answering questions or summarizing information.

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.

Zero-Trust architecture is a security framework that assumes all users, devices, and transactions are potential threats and nothing can be trusted implicitly, therefore requiring strict authentication and authorization measures for every access attempt. A Zero Trust approach is a core pillar of most enterprise cyber security strategies, resulting in strengthened defense against cyber threats, enhanced data protection, and continuous security monitoring and enforcement across its network and systems.

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.

Connected data models involve networks of data points or nodes linked through relationships. Knowledge graphs are a popular way to do this, making connections between disparate sources to provide specific insights. They aim to intuitively represent the interconnected world. The real world is flexible, messy and constantly changing. Our relationships and connections are dynamic and are at times incredibly complex and layered, and knowledge graphs are designed to reflect this complexity.

A data catalog is the ability to inventory and organize data assets. Capabilities include using machine learning for automatically detecting relationships between data assets. This process involves users verifying and resolving any uncertainties found during automated inventory.

A data control engine is an external system that uses metadata and relationships in a graph to enforce enterprise policies, manage governance, and control access across applications and systems. It ensures that rules, trust signals, and usage restrictions travel with the data, allowing operationalized graph data to be used securely and consistently in analytics, AI, and workflows.

Why it matters: Ensuring policies and governance travel with the data prevents misuse, reduces compliance risk, and allows secure, scalable data operations.

Data mesh is a data management approach that supports a domain-led practice for defining, delivering, maintaining, and governing data products. While it’s not yet an established best practice, data mesh helps ensure that data products are easy to find and use by data consumers, such as business users, data analysts, data engineers, or other systems. Additionally, data products must meet terms of service and SLAs, forming a contract between the provider and the consumer.

A graph model is a way of structuring data (using a graph database) that represents entities as nodes and the relationships between them as edges. It captures not just values but the connections and dependencies among entities, allowing enterprises to see complex systems, follow chains of interaction, and understand how elements such as customers, accounts, transactions, and products relate to one another.

‍Why it matters: Understanding and using graph models enables organizations to uncover hidden relationships, maintain context across systems, and make better-informed decisions.

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.

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.

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-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.

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.

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.

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 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.

A data fabric is a design framework for creating flexible and reusable data pipelines, services, and semantics. It uses data integration, active metadata, knowledge graphs, profiling, machine learning, and data cataloging. Data Fabric changes the main approach to data management, which is “build to suit” for data and use cases and replaces it with “observe and leverage”.

Data governance is a framework of rules and guidelines for how everyone should handle and use information in a company to keep it accurate, secure, and useful.

Why it matters: Strong governance ensures data is secure, compliant, and used responsibly, reducing risk while enabling business value.

Data integration involves practices, techniques, and tools to ensure consistent access and delivery of data across different areas and structures within a company. It aims to meet the data needs of all applications and business processes efficiently.

Why it matters: Seamless data integration ensures consistency, reduces duplication, and supports accurate, organization-wide insights.

Data lineage refers to the lifecycle and journey of data from origin to destination. From creation to how it’s been edited, transformed and used. Data lineage is critical to know how data can and should be used (compliance), how it was generated and how trustworthy it is. This becomes particularly important when considering data for insights or for machine learning and large language models.

Data management refers to the collection, organization, protection and utilization of an organization’s data and is a core enabler of modern businesses. Data is considered a company’s most critical and valuable asset, however without tooling to effectively manage and make use of the data, it is worthless. Data management technologies include Master Data Management, Customer Data Platforms, Data Unification platforms and Data integration platforms. Every system in use at an enterprise collects data, so a clear data management strategy is crucial to manage, govern and make use of all the data in a safe, secure and compliant way.

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.

Learn how to protect agaist data poisioning here.

Data profiling involves statistical analysis of various datasets (both structured and unstructured, external and internal) acting as an enabler to provide business users with insights into data quality and identify data quality issues. Profiling also checks data against established rules from rules management.

Data provenance is a historical record of source data, a way to understand the journey of data throughout the organization. Data provenance plays a crucial role in understanding the quality of your data and ensuring its veracity. It’s like a detailed travel log for data, showing where it came from, where it has been, and how it has changed over time.‍

Why it matters: Knowing a data’s origin and transformations ensures accuracy, trust, and compliance across systems and applications.

Data risk scoring is a method of rating potential risk on different kinds of information based on how sensitive it is and how likely it could be accessed by someone who shouldn't have it.

Why it matters: Understanding potential data risks allows organizations to prioritize mitigation and protect sensitive information.

Data transformation refers to the process of converting data from one format or structure into another, often done to facilitate analysis, integration or storage. Similarly we could say it’s like changing a piece of Lego so they fit better together in your creation, or in order to build something new and useful.

Data trust scoring assesses the reliability of any data with standards that provide instant insight into how much you can trust your data.

Data veracity refers to the accuracy, quality, and reliability of data, in order to make it suitable for decision-making and analysis. The better data veracity, the more trustworthy and better performing your AI can be, for instance.

Why it matters: Reliable, high-quality data is critical for accurate insights, trustworthy AI, and informed decision-making.

Data visibility in AI involves having a clear and complete understanding of the data used by AI models - where it originates, how it’s processed, and how it influences the AI’s decisions. This helps organizations ensure data quality, maintain accountability, and make better, more transparent AI-driven decisions.

‍Why it matters: Understanding how data shapes AI behavior enables transparency, fairness, and responsible governance across AI systems.

Data visibility refers to the ability to view and understand data across systems or platforms. It ensures users can easily locate, access and interpret data, providing transparency and supporting informed decision-making.

‍Why it matters: Greater visibility supports informed decision-making, improves accountability, and helps detect gaps or risks in how data is used.

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.

Dynamic authorization is a context-based decision model that grants or denies access in real-time, rather than relying solely on static, predefined permissions. It works by first identifying the nature of the request, before deciding whether to collect any additional data to make the authorization decision. The process is done dynamically in real-time, and after collecting all context needed the right decision is made, as defined by the application’s access policies. Access is either granted, denied, or more information might be requested. For customers it can enable minimal friction.

Learn more here.

Externalized authorization is access control decisions centralized and separated from application logic. In other words, it centralizes access control decisions for applications and systems across the organization, rather than within individual programs. This means the access logic and policies are consistent, regardless of the application.  It’s like having a central security office that decides who can enter which rooms in all buildings of a company, instead of each building managing its own security. Such centralized management allows security and IAM professionals to efficiently add, update and deploy policies across a portfolio of applications, alongside fine-grained access control which ensures users access the right data and actions. When combined with a dynamic data model, it allows businesses to leverage other data, make faster decisions based on dynamic data points and orchestrate a consistent experience across services (with all systems using the same externalized authorization). 

Learn more here.

Or check out this webinar.

Fine-grained access control allows for more precise management of access permissions, and grants or denies access based on multiple factors. This method provides precise control over who can access what data or functionalities, and becomes particularly important when dealing with access to specific data or in complex circumstances - where you might have more than one account. Imagine a library where access to each section and book is individually controlled. Some books may only be available to specific membership types, and users may need different permissions to borrow books or access special collections.

Learn more here.

First party data refers to information directly collected by a company from its customers or users. It is typically obtained through interactions, transactions, or engagement with the company's own platforms, products, or services. First-party data includes information from sources like your customer relationship management (CRM) system.

Fragmented data refers to data that is scattered across multiple sources or systems in a disorganized manner, making it difficult to access, analyze and use. A company may use different systems for sales, inventory, CRM, marketing, etc. Without this data unified, it can be difficult to get a complete picture, leading to poor decision making and unsatisfied customers.

Graph data modeling is the process of structuring data as a graph, where entities are represented as nodes and their relationships as edges with attributes. This approach carries context with the data, supports flexibility as business requirements evolve, and enables visibility across connected domains, forming a foundation for analysis, governance, and operational use.

Graph data is information organized as nodes and edges, where nodes represent entities (people, accounts, devices) and edges represent relationships (ownership, interaction, dependency). Both nodes and edges can carry attributes, allowing the data to include context alongside values. This structure makes relationships explicit and queryable, enabling enterprises to see dependencies, patterns, and connections that traditional tabular data cannot capture.

Graph integration is the use of a graph model as a shared layer to connect data from multiple systems while preserving the relationships between entities. Rather than moving isolated records, applications operate on a connected structure that reflects how systems, customers, and processes interact. This approach improves consistency, adaptability, and context-rich insights across enterprise applications, while embedding governance and reducing duplication or conflicts.

Why it matters: Integrating data through a graph model preserves context, reduces duplication, and supports faster, more accurate insights across systems.

Graph-based access control is a security model that uses graph technology - where nodes represent entities (like users, roles, or resources) while edges represent relationships - to manage and enforce access decisions. By analyzing these relationships, the system can determine whether a user should be granted access based on context, connections, and permissions.

‍Why it matters: Graph-based access enables more accurate, context-rich decisions, improving both security and flexibility in complex environments.

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.