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.

Dynamic enforcement is the practice of applying policies, permissions, or controls in real time, based on current conditions, context, or risk factors. Unlike static rules, it adapts continuously to changing circumstances to maintain security, compliance, and operational efficiency.

Why it matters: Dynamic enforcement reduces the risk of unauthorized actions, ensures policies remain effective in complex environments, and supports responsive decision-making. In AI systems, it is especially critical for autonomous agents, where actions occur at machine speed and must be continuously evaluated against evolving conditions.

Enterprise knowledge search builds on enterprise search by enabling stakeholders not only to find information but also to understand and apply it. It connects and interprets data across all internal sources - such as documents, emails, databases, and collaboration tools - to surface meaningful insights and synthesized answers. Modern versions use AI to understand context, relationships, and intent, turning raw data into accessible knowledge.

‍Why it matters: Enterprise knowledge search helps teams go beyond locating files to discovering the knowledge within them, improving decision-making, collaboration, and productivity across the organization.

Enterprise search is technology that enables organizations to find information stored across internal systems, such as file shares, intranets, document repositories, and databases. It indexes and retrieves content based on keywords and metadata, providing users with lists of relevant documents or data sources from across the enterprise.

‍Why it matters: Enterprise search helps employees locate information efficiently, reduces time spent navigating siloed systems, and creates a unified way to access organizational data.

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.

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.

Real-time data visibility essentially means live information. It refers to the capability of accessing and analyzing data as it is generated or updated, providing immediate insights into current business operations or conditions. It’s like watching a sports game on your phone. You can see the score, and plays as they happen in real time. For a company, being able to have real-time visibility and insights on their data, offers many opportunities such as facilitating proactive decision-making, and improving overall efficiency.

Why it matters: Immediate access to live data enables faster decision-making, proactive management, and better operational control.

Secure model coordination ensures multiple AI models or agents collaborate safely, sharing data and instructions while maintaining integrity, privacy, and compliance.

Why it matters: Proper coordination prevents errors, miscommunications, or malicious interference in multi-model environments.

Structured data is information that is carefully organized according to well-defined schemas, often stored in databases with rows and columns. Organizations invest in governance practices such as catalogs, metadata tagging, accuracy checks, and lineage tracking to ensure reliability. Examples include financial records, CRM entries, transaction logs, and operational metrics.

‍Why it matters:Structured data has been the cornerstone of enterprise analytics for decades because it is predictable, consistent, and easy to query. It supports reporting, forecasting, simulations, and traditional machine learning models. However, it does not capture the depth of context and nuance found in unstructured data, which is critical for AI-native applications and LLM-driven workflows.

Technical debt refers to the total expense caused by inadequate architecture or software development. These may be decisions to prioritize speed over design, but it is more often the result of short-sighted, siloed software decisions without a view to the broader architecture. Legacy solutions that have become obsolete over time, but are incorporated in a way that is difficult to remove also contributes to an organization’s technical debt.

The AI lifecycle is the end-to-end process of developing, deploying, and maintaining an AI system. It includes stages such as problem definition, data preparation, model training, evaluation, deployment, monitoring, and ongoing governance to ensure performance, accuracy, and compliance over time.

‍Why it matters: Managing the full lifecycle ensures AI systems remain accurate, secure, compliant, and aligned with business goals over time.

Trust fabric is a concept primarily driven by Microsoft, defined as a real-time approach to securing access that is adaptive and comprehensive. A trust fabric authenticates identities, verifies access conditions, checks permissions, encrypts the communication channel, and monitors for security breaches. All continuously evaluated in real-time.

Trusted data use is the practice of using data with confidence that it is accurate, reliable, complete, and governed. It ensures that data meets defined quality, security, and compliance standards, allowing it to be used safely across systems, analytics, and AI.

Why it matters: Trusted data use enables consistent, compliant, and confident decisions, ensuring that data-driven systems operate safely and effectively.

Unauthorized inference refers to the act of gaining access to sensitive information by exploiting an AI system’s responses or behavior, without direct access to the underlying data. It takes advantage of the possibility that public information may reveal hints about concealed data or system behaviors.

‍Why it matters: Preventing unauthorized inference protects sensitive information from being indirectly exposed through model outputs.

Unified data refers to the alignment of data from various sources into a single, coherent view, enabling comprehensive analysis and decision-making. Unifying data across the organization provides an opportunity to remove both technical and organizational silos, gain greater understanding and insight and enable better business outcomes.

Unstructured data is information that does not follow a predefined schema or table format, which makes it difficult to organize, query, or govern using traditional database tools. It exists in many forms, including documents such as contracts and manuals, emails and chat messages, and rich media like images and videos. This content is often spread across file systems, cloud drives, and collaboration platforms, usually without consistent tagging or governance.

Why it matters:
Unstructured data holds most of an organization’s meaningful context and institutional knowledge, which makes it essential for AI systems that rely on language, reasoning, and interpretation. As LLM driven applications become standard, unstructured content shifts from a loosely managed resource to a critical input for accurate, secure, and scalable AI.