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.

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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.

RagProtect secures Retrieval-Augmented Generation (RAG) systems by providing fine-grained, context-aware authorization. It ensures that only the right information is accessed in the right context, protecting sensitive data and preventing leaks during AI interactions.

Why it matters: RAG systems often handle confidential data; RagProtect safeguards privacy, compliance, and trust in AI-driven outputs.

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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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.

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 schema is the formal definition of what data exists within a system and how it is structured, including data types, constraints, and relationships. It sets expectations for how data should be stored, validated, and consumed.

Why it matters:
Schemas provide consistency and predictability, allowing AI systems to know what data to expect and how to handle it. This structure reduces errors, supports governance, and enables reliable integration across systems.

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.

A semantic layer is a business-friendly abstraction layer that defines shared concepts, metrics, and definitions on top of raw data. It translates technical data structures into consistent, human and machine understandable meaning, ensuring that both people and AI systems interpret numbers and metrics the same way.

Why it matters:
By establishing agreed definitions and metrics, a semantic layer prevents misinterpretation and disagreement over what data represents. This consistency is essential for AI systems, which rely on clear meaning to reason accurately and deliver trustworthy results at scale.

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.