Organizational Implementation Considerations:

Before starting on the technical journey from “RAGs to Riches”, there are several considerations for an organization before, during, and after creating a production solution. By taking into account each of these considerations, a production LLM solution has a much higher chance of success.

Before: Aligning Business Outcomes

Prior to building out a production LLM solution, a team will have developed a PoC LLM solution that is able to answer a limited set of use cases. Before the start of production development, it is imperative that business outcomes and the priorities of key stakeholders are aligned with project goals. This often looks like mapping business outcomes – such as enhanced customer interactions, operational efficiency, or reduced compliance risk to quantifiable outcomes such as shorter response times and findability of information. It is important to ensure these business goals translate from development to production and adoption by customers. Besides meeting technical functionality, setting up clear customer and organizational goals will help to ensure the production LLM solution continues to have organizational support throughout its entire lifecycle.

During: Training Talent and Proving Solutions

Building out a production LLM solution will require a team with specialized skills in natural language processing (NLP), prompt engineering, semantic integration, and embedding strategies. In addition, EK recommends investing resources into content strategists and SMEs who understand the state of their organization’s data and/or content. These roles in particular are critical to help prepare content for AI solutions, ensuring the LLM solution has comprehensive and semantically meaningful content. Organizations that EK has worked with have successfully launched and maintained production LLM solutions by proactively investing in these skills for organizational staff. This helps organizations build resilience in the overall solution, driving success in LLM solution development.

After: Infrastructure Planning and Roadmapping

To maintain a production LLM solution after it has been deployed to end-users, organizations must account for the infrastructure investments and operational costs needed, as well as necessary content and data maintenance. Some of these resources might include enterprise licensing, additional software infrastructure, and ongoing support costs. While many of these additional costs can be mitigated by effectively aligning business outcomes and training organizational talent, there still needs to be a roadmap and investment into the future infrastructure (both systematically and content-wise) of the LLM production solution.

Technical Criteria for Evaluating LLM PoCs:

In parallel with the organizational implementation considerations, and from EK’s depth of experience in developing LLM MVPs, designing enterprise AI architecture, and implementing more advanced LLM solutions such as Semantic RAG, EK has developed 7 key dimensions that can be used to evaluate the effectiveness of an LLM PoC:

Figure 1: Dimensions for Evaluating an LLM Solution

1. Depth of Interaction: refers to how deeply and dynamically users can engage with the LLM solution. At a lower level, interaction might simply involve asking questions and receiving direct answers, while at the highest level, intelligent agents act on behalf of the user autonomously to leverage multiple tools and execute tasks.

2. Freshness of Information: describes how frequently the content and data behind the semantic search solution are updated and how quickly users receive these updates. While lower freshness implies data updated infrequently, at higher freshness levels, data is updated frequently or even continuously which helps to ensure users are always interacting with the most current, accurate, and updated information available.

3. Level of Explanation: refers to how transparently the LLM solution communicates the rationale behind its responses. At a lower level of explanation, users simply are receiving answers without clear reasoning. In contrast, a high level of explanation would include evidence, citations, audit trails, and a clear path on how information was retrieved. 

4. Personalization, Access & Entitlements Requirements: describes how specifically content and data are tailored and made accessible based on user identity, roles, behavior, or needs. At lower levels, content is available to all users without personalization or adaptations. At higher levels, content personalization is integrated with user profiles, entitlements, and explicit access controls, ensuring users only see highly relevant, permissioned content. 

5. Accuracy of Information: refers to how reliably and correctly the LLM solution can answer user queries. At lower levels, users receive reasonable answers that may have minor ambiguities or occasional inaccuracies. At the highest accuracy level, each response is traced back to original source materials and are cross-validated with authoritative sources. 

6. Enterprise Agentic Support: describes how the LLM solution interacts with the broader enterprise AI ecosystem, and coordinates with other AI agents. At the lowest level, the solution acts independently without any coordination with external AI agents. At the highest level, the solution seamlessly integrates as a consumer and provider within an ecosystem of other intelligent agents.

7. Enterprise Embedding Strategy: refers to how the LLM solution converts information into vector representations (embeddings) to support retrieval. At a lower level embeddings are simple vector representations with minimal or no structured metadata. At the highest levels, embeddings include robust metadata and are integrated with enterprise context through semantic interpretation and ontology-based linkages. 

For an organization, each of the technical criteria will be weighed differently based on the unique use cases and requirements of the LLM solution. For example, an organization that is working on a content generation use case could have a greater emphasis on Level of Explanation and Freshness of Information while an organization that is working on an information retrieval use case may care more about Personalization, Access, & Entitlements Requirements. This is an integral part of the evaluation process, with an organization coming to agreement on the level of proficiency needed within each factor. Leveraging this standard, EK has worked with organizations across various industries and diverse LLM use cases to optimize their solutions.

Additionally, EK recommends that an organization undergoing an LLM PoC evaluation also conduct an in-depth analysis of content relevant to their selected use case(s). This enables them to gain a more comprehensive understanding of its quality – including factors like completeness, relevancy, and currency – and can help unearth gaps in what the LLM may be able to answer. All of this informs the testing phase by guiding the creation of each test, as well as the expected outcomes, and can be generally categorized across three main areas of remediation:

• Content Quality – The content regarding a certain topic doesn’t explicitly exist and is not standardized – this may necessitate creating dummy data to enable certain types of tests.
• Content Structure – The way certain content is structured varies – we can likely posit that one particular structure will give more accurate results than another. This may necessitate creating headings to indicate clear hierarchy on pages, and templates to consistently structure content. 
• Content Metadata – Contextual information that may be useful to users is missing from content. This may necessitate establishing a taxonomy to tag with a controlled vocabulary, or an ontology to establish relationships between concepts. 

Technical Evaluation of LLM PoCs In Practice:

Putting the organizational implementation and technical considerations into practice, EK recently completed an engagement with a leading semiconductor manufacturer, employing the standard process for evaluating their PoC LLM search solution. The organization had developed a PoC search solution that was being utilized for answering questions against a series of user-selected PDFs relating to the company’s technical onboarding documentation. EK worked with the organization  to align on key functional requirements via a capability assessment for a production LLM solution based on the 7 dimensions EK has identified. Additionally, EK completed a simultaneous analysis of in-scope content for the use case. The results of this content evaluation informed which content components should be prioritized and candidates for the testing plan.

After aligning on priority requirements, in this case, accuracy and freshness of information, EK developed and conducted a testing plan for parts of the PoC LLM. To operationalize the testing plan, EK created a four-phase RAG Evaluation & Optimization Workflow to turn the testing plan into actionable insights.This workflow helped produce a present-state snapshot of the LLM solution, a target-state benchmark, and a bridging roadmap that prioritizes retriever tuning, prompt adjustments, and content enrichment. Based on the workflow results, stakeholders at the organization were able to easily interpret how improved semantics, content quality, structure, and metadata would improve the results of their LLM search solution.

In the following blogs of the “RAGs to Riches” series, EK will be explaining the process for developing a capability assessment and testing plan for LLM based PoCs. These blogs will expand further on how each of the technical criteria can be measured as well as how to develop long-term strategy for production solutions.

Conclusion

Moving an LLM solution from proof-of-concept to enterprise production is no small feat. It requires careful attention to organizational alignment, strong business cases, technical planning, compliance readiness, content optimization, and a commitment to ongoing talent development. Addressing these dimensions systematically will ensure that your organization will be well positioned to turn AI innovation into a durable competitive advantage.

If you are interested in having EK evaluate your LLM-based solution, and help build out an enterprise-grade implementation contact us here. 

The post LLM Solutions PoC to Production: From RAGs to Riches (Part 1) appeared first on Enterprise Knowledge.

• AI tools make data in all forms more accessible than ever before.
• Data is captured in a broader range of tools (both in the cloud and on-premises), each with its own security model.
• Hackers are more sophisticated than ever, and the need for highly decentralized information repositories with strong security models is now seen as a critical way to deter them.

In the same way that we now have technologies that enable better information access, we also have technologies that make securing this information more robust and scalable. You can learn more about how this is done in our blog post, “Inside the Unified Entitlements Architecture.” In this article, we describe how a Unified Entitlements Service (UES) can be set up to consistently replicate information access rules from a central source across a wide range of products so that these rules are the same throughout the organization. 

As with most problems, technology is only part of the solution. Implementing a UES is not merely a technical project, but a transformational journey. As part of this journey, organizations typically progress through several maturity stages:

• Discovery and Assessment: Mapping the current entitlement landscape across platforms and identifying the highest-risk inconsistencies.
• Policy Standardization: Creating a unified policy framework that translates business rules into technical controls.
• Incremental Implementation: Rolling out UES capabilities gradually, starting with the most critical data sources and expanding over time.
• Continuous Improvement: Refining policies, enhancing performance, and expanding coverage to new data platforms as they enter the enterprise ecosystem.

The Discovery and Assessment stage is critical to understanding the complexity of implementing unified entitlements across an organization. During this stage, analysts identify which repositories need content with specific entitlement rules, the rules that need to be described, and how they will be implemented. Most organizations focus on securing their datasets and SharePoint online. While that is a good starting point, there are many other repositories that likely need to be properly secured. Information like contracts, client data, pricing, and product specifications may all require their own security policies. It is important to put together a list of these repositories and their business owners so that the true scope of the problem is understood correctly. Once this list is in place, the security rules (or policies) can be enumerated. These rules might look like the following:

Limit access to client team members, the project sponsor, and senior leadership only

This list of rules for different information assets should be understandable by both business and technical people and is often quite lengthy. Having discovered the repositories and established the rules, it is important to identify who is responsible for ensuring these rules are in place both at the time of the analysis and in the future. Once this discovery work is complete, the entitlements team can start to move into iterative project implementation.

After defining the repositories and rules, the Policy Standardization process begins. During this stage, the security rules defined in the first stage are aligned with the systems to which they apply to, and the security policy models are developed. Each system has its own way of managing security, and the new security policy models need to account for these requirements. Since most security models are either role-based or attribute-based, the new policy models typically address requirements for groups and attributes at an enterprise level. One of the key outputs of this stage are the guidelines for how groups need to be managed and what personal attributes need to be captured, managed, and shared with other applications.

After a core set of policies are defined, the Incremental Implementation stage can begin. During this stage, IT works with repository owners to automate the application of entitlements using the UES. This is a collaborative effort where IT implements the rules to automate entitlements, and business users identify the exceptions that inevitably arise. Both parties then work through the exceptions until the entitlements are correct. Then, this process is repeated with other repositories across the enterprise, focusing on the most critical repositories first.

The Continuous Improvement stage begins once the initial implementations are completed. Information management should never be static. As new information types are captured, new systems are implemented, and new security policies are required, the entitlements must be updated. We help our clients define a repeatable process to update their UES with the latest policies to keep their entitlements aligned with continuously changing business needs.

This journey yields progressive benefits at each stage, from reduced administrative overhead to enhanced security and an improved compliance posture. Organizations that successfully navigate this transformation gain not just better governance but a strategic advantage: the ability to safely democratize data access while maintaining robust protection for sensitive information.

Our Unified Entitlements team has helped others through this journey. If you want to solve your entitlement problems, please contact our team for guidance at info@enterprise-knowledge.com.

The post The Journey to Unified Entitlements appeared first on Enterprise Knowledge.

Effective management of information access across the enterprise is one of the most difficult problems that large organizations deal with today. Unified entitlements offer a solution by providing a comprehensive definition of access rights, ensuring consistent and correct privileges across every system and asset type in the organization.

A Unified Entitlements Service (UES) addresses these challenges by creating a centralized policy management system. It translates high-level business rules into controls specific to each platform. UES acts as the universal translator for security policies, allowing governance teams to define rules once and apply them everywhere.

A strong UES consists of several interlocking components that work together to provide seamless policy enforcement while still respecting each platform’s native security model. The diagram below illustrates how these components interact in a comprehensive UES implementation:

Figure 1. High-level architecture of a Unified Entitlements Service showing the key components and their interactions

The Core Components

Entitlement Integration Core: This stateless microservice cluster serves as the brain of the UES, managing the complex relationships between users, roles, and permissions. It utilizes high-performance caching (typically implemented with Redis or similar technologies), it provides entitlement lookups to maintain performance.

Policy Engine: Built on frameworks like Open Policy Agent (OPA), this component evaluates access requests against enterprise-wide policies expressed in a domain-specific language. For example, a policy might state: “Users in the Marketing department can access customer demographic data, but not payment information, unless they also belong to the Finance team and are working on the Q4 campaign.”

Provenance & Lineage Tracking: Every access decision is logged with comprehensive context, creating an immutable audit trail for compliance and security investigations. Implementations typically leverage systems like Apache Atlas alongside Kafka Streams for real-time audit logging.

Query Federation Layer: Beyond simply enforcing access at the resource level, advanced UES implementations apply entitlements directly to query execution. Using technologies like Trino (formerly PrestoSQL) with custom connectors, the system can modify queries in-flight to add entitlement-aware filters.

Entitlement Integrations: These connectors translate UES decisions into platform-specific access controls within native Identity and Access Management (IAM) systems. This typically involves the use of OAuth 2.0 and SAML for authentication flows.

Metadata Management Portal: A user-friendly interface empowers governance teams to define, test, and monitor entitlement policies. Modern implementations often use React-based front-ends with GraphQL APIs to provide a responsive management experience.

The Lifeblood of UES: Entity Resolution

At the heart of effective entitlement management lies a critical challenge: accurately resolving user identities across disparate systems. A single individual might exist as three distinct identities, such as:

• john.smith@company.com in Azure AD
• jsmith_finance in Snowflake
• employee_456789 in AWS IAM

Without proper resolution, John might inadvertently gain excessive privileges through the combination of his separate identities or face frustrating access denials where legitimate access should be granted.

A sophisticated UES employs entity resolution algorithms—combining deterministic matching rules, probabilistic methods, and sometimes machine learning—to create a unified identity graph. Products like Senzing are designed for this very purpose. This graph connects all representations of a user across systems, enabling consistent policy enforcement regardless of which system they’re accessing.

The resulting unified user profile might look like this:

This unified view becomes the foundation for consistent entitlement decisions across the entire data ecosystem.

Architectural Pattern for Enterprise Deployment

Federated Enforcement with Local Agents

The Unified Entitlement Service employs a layered and federated architecture designed for scalability, interoperability, and governance across enterprise data environments. At its core, the system is structured into distinct layers, each responsible for key functions:

• Entitlement Integration Core Service (EIS) manages access control, policy enforcement, and lineage tracking.
• Metadata Management Service ensures governance and transparency.
• Query Federation enables distributed query execution.
• Entitlement Integrations provide seamless access to diverse data sources.

This architecture diverges from the traditional hub-and-spoke model, operating as a federated governance framework. In this model, entitlement decisions are enforced dynamically across multiple platforms without centralizing sensitive data. The Distributed Query Engine plays a crucial role in aggregating results across entitlement sources, ensuring that governance policies are applied at the time of query execution.

Real-World Implementation Challenges

Despite its compelling benefits, implementing a UES presents significant challenges that organizations must carefully navigate.

Case Study

In recent work with a large global investment firm, we implemented role-based access control (RBAC) and attribute-based access control (ABAC) as one component of a unified entitlements solution. In this work, graph data was persisted in a Neo4j database. Read and traversal entitlements for properties were implemented to control what nodes were discoverable, and what properties of nodes were viewable in downstream applications. Through single sign-on (SSO) connections to Neo4j, a UES can maintain awareness of data source grants while implementing higher level entitlements.

Policy Drift

Without proper controls, UES policies may diverge from actual platform rules. For example, a database administrator might make an emergency change directly in PostgreSQL, bypassing the UES. Over time, these discrepancies accumulate, creating security gaps.

Solution: Implement continuous compliance scanning that compares actual platform entitlements against UES policies, flagging and remediating discrepancies.

Performance Considerations

Real-time entitlement validation adds overhead to data access requests. For analytical workloads processing billions of records, even milliseconds of added latency per decision can significantly impact performance.

Solution: Employ a hybrid approach that combines pre-computed access decisions for common patterns with just-in-time validation for edge cases. Aggressive caching of entitlement decisions can reduce overhead to negligible levels for most scenarios.

Organizational Alignment

Perhaps the most overlooked challenge is organizational: UES crosses traditional boundaries between security, data, and platform teams. Without clear ownership and governance, implementation efforts can stall amid competing priorities.

Solution: Establish a federated governance model with representatives from security, data management, compliance, and platform engineering. This cross-functional team should own the UES strategy and roadmap, ensuring alignment across organizational boundaries.

The Future of Unified Entitlements

As UES technology matures, several emerging trends point to its future evolution:

AI-Driven Entitlement Intelligence: Advanced UES implementations are beginning to incorporate machine learning to detect anomalous access patterns, suggest policy improvements, and automatically remediate compliance gaps. These capabilities will transform UES from a passive enforcement layer to an active participant in security governance.

Context-Aware Access Policies: Next-generation entitlement systems will incorporate contextual factors beyond identity—such as device health, location, time of day, and behavioral patterns—to make more nuanced access decisions. For example, a finance analyst might have full access to sensitive data when working from corporate headquarters but receive masked results when connecting from a coffee shop.

Federated Multi-Cloud Governance: As enterprises adopt multi-cloud strategies, UES will evolve to provide consistent governance across cloud boundaries, ensuring that security policies remain portable even as workloads move between environments.

Conclusion: A Services Based Approach

Managing entitlements in a consistent manner across all of your applications, both on-premises and in the cloud, feels like an impossible challenge. As a result, many organizations avoid the problem, hoping it will resolve itself. A services-oriented approach like the one that described above makes solving this problem possible. If you would like to learn more about how this works and how you can solve entitlements at your organization, please email us at info@enterprise-knowledge.com.

The post Inside the Unified Entitlements Architecture appeared first on Enterprise Knowledge.

These aren’t hypothetical situations. They happen daily across enterprises that have invested millions in data infrastructure but neglected the crucial layer that governs who can access what data, when, and how. As organizations expand their data ecosystems across multiple clouds, databases, and analytics platforms, the challenge of maintaining consistent access control becomes exponentially more complex. This review provides a technical follow-up to the concepts outlined in Why Your Organization Needs Unified Entitlements and details the architecture, implementation strategies, and integration patterns needed to build a robust Unified Entitlements System (UES) for enterprise environments. I will address the complexities of translating centralized policies to platform-specific controls, resolving user identities across systems, and maintaining consistent governance across cloud platforms.

The Entitlements Dilemma: A Perfect Storm

Today’s enterprises face a perfect storm in data access governance. The migration to cloud-native architectures has created a sprawling landscape of data sources, each with its own security model. A typical enterprise might store customer data in Snowflake, operational metrics in PostgreSQL, transaction records in MongoDB, and unstructured content in AWS S3—all while running analytics in Databricks and feeding AI systems through various pipelines.

This diversity creates several critical challenges that collectively undermine data governance:

Inconsistent Policy Enforcement: When a new employee joins the marketing team, their access might be correctly configured in Snowflake but misaligned in AWS Lake Formation due to differences in how these platforms structure roles and permissions. Snowflake’s role-based access control model bears little resemblance to AWS Lake Formation’s permission structure, making uniform governance nearly impossible without a unifying layer.

Operational Friction: Jennifer, a data governance officer at a financial services firm, spends over 25 hours a week manually reconciling access controls across platforms. Her team must update dozens of platform-specific policies when regulatory requirements change, leading to weeks of delay before new controls take effect.

Compliance Blind Spots: Regulations like GDPR, HIPAA, and CCPA mandate strict data access controls, but applying them uniformly across diverse platforms requires expertise in multiple security frameworks. This creates dangerous compliance gaps as platform-specific nuances escape notice during audits.

Identity Fragmentation: Most enterprises operate with multiple identity providers—perhaps Azure AD for corporate applications, AWS IAM for cloud resources, and Okta for customer-facing services. Without proper identity resolution, a user might exist as three separate entities with misaligned permissions.

Beyond Simple Access Control: The Semantics Challenge

The complexity doesn’t end with technical implementation. Modern AI workflows rely on a semantic layer that gives meaning to data. Entitlement systems must understand these semantics to avoid breaking critical data relationships.

Consider a healthcare system where patient records are split across systems: demographics in one database, medical history in another, and insurance details in a third. A unified approach to managing entitlements should be developed to understand these semantic connections and ensure that when doctors query patient information, they receive a complete view according to their access rights rather than fragmented data that could lead to medical errors.

The Unified Entitlements Solution

A UES addresses these challenges by creating a centralized policy management system that translates high-level business rules into platform-specific controls. Think of it as a universal translator for security policies—allowing governance teams to define rules once and apply them everywhere.

How UES Transforms Entitlement Management

Let’s follow how a UES transforms the experience for both users and administrators:

For Maria, the Finance Analyst: When she logs in through corporate SSO, the UES immediately identifies her role, department, and project assignments. As she queries the data lake, the UES dynamically evaluates her request against centralized policies, translating them into AWS Lake Formation predicates and Snowflake secure views. When she exports data to Excel, column-level masking automatically obscures sensitive fields she shouldn’t see. All of this happens seamlessly without Maria even knowing the UES exists.

For the Data Governance Team: Instead of managing dozens of platform-specific security configurations, they define policies in business terms: “Finance team members can access aggregated revenue data but not customer PII” or “EU-based employees cannot access unmasked US customer data.” The UES handles the complex translation to platform-native controls, dramatically reducing administrative overhead.

Conclusion: The New Foundation for Data Governance

As enterprises continue their data-driven transformation, a UES emerges as the essential foundation for effective governance. UES enables organizations to enforce consistent access rules across their entire data ecosystem by bridging the gap between high-level security policies and platform-specific controls.

The benefits extend beyond security and compliance. With a properly implemented UES, organizations can accelerate data democratization while remaining confident that appropriate guardrails are in place. They can adopt new data platforms more rapidly, knowing that existing governance policies will translate seamlessly. Most importantly, they can unlock the full value of their data assets without compromising on protection or compliance.

In a world where data is the lifeblood of business, unified entitlements isn’t just a security enhancement—it’s the key to unlocking the true potential of enterprise data.

The post Unified Entitlements: The Hidden Vulnerability in Modern Enterprises appeared first on Enterprise Knowledge.

An entitlement management model is an important part of data governance. Unified entitlements provide a holistic definition of access rights. You can read more about the value of unified entitlements here. This model can be designed and implemented within a semantic layer, providing an organization with roles and associated permissions for different types of data users. Below is an example of an organizational entitlements model with roles, and explanations of an example role for fictional user Carol Jones.

Having a consistent and predictable approach to entitlements within a semantic layer framework makes decisions easier for human administrators within a data governance framework. It helps to alleviate questions around how to gain access to information needed for projects if it is not already available to a user, given their entitlements. Clearly defined, consistent, and transparent entitlements provide greater ease of access for users and stronger security measures for user access. The combination of reduction in risk and reduction in lost time makes entitlements an essential area of any enterprise semantic layer framework.

	Efficiency New users are able to be onboarded with the correct permissions sooner by an administrator with a clear understanding of the permissions this new user needs. As the user’s role evolves, they can submit requests for increased permissions.
	Risk Mitigation Administrators and business leads at a high level within the framework are able to see all of the users in a business area and their associated permissions within the semantic layer framework. If the needs of the user change, or as users leave the company, the administrator can quickly and easily remove permissions from the user account. This method of “pruning” permissions within an entitlements model reduces risk by mitigating the chance of users maintaining permissions to information they no longer need.
	Diagnostics In a data breach, the point of entry can be quickly identified.
	Identify Points of Contact Users who can see the governance model can quickly identify points of contact for specific business areas within an organization’s semantic layer framework. This facilitates communication and collaboration, enabling users to see points of contact to permission areas across the organization.

An entitlement management model addresses the issue of “which users can do what” with the organization’s data. This is commonly addressed by considering which users should be able to access (read), edit (write, update), or create and delete data, often abbreviated as CRUD. Another facet of the data that must be considered is the visibility users should have. If there are parts of the data that should not be seen by all users, this must be accounted for in the model. There may be different groups of users with read permissions, but not for all the same data. These permissions will be assigned via roles, granted by users with an administrative role. 

C=Create, R=Read, U=Update, D=Delete

One method to solve this problem is to develop a set of heuristics for users that the administrator can reference and revise. By having examples of the use cases that they have granted permissions for, they can reference these when deciding what permissions to grant new users within a model, or users whose data needs have evolved. It is difficult to predict all individual user needs, especially as an organization grows and as technology advances. Implementing a set of user heuristics allows administrators to be consistent in granting user permissions to semantically linked data. They are able to mitigate risk and provide appropriate access to the users within the organization. The table below shows some common heuristics, who to apply them to and a decision if the entitlements needs further review. A similar approach is the Adaptable Rule Framework (ARF).

This method serves as a precursor to documenting a formal process for entitling, which should include the steps, sequence, requirements, and timeliness in which users are entitled to access data augmented by a semantic layer. These entitlements will determine where in the semantic layer framework users can go and their ability to impact the framework through their actions. Decisions and documentation of these process elements provide thorough consistency within an organization for managing entitlements.

Enterprise Knowledge (EK) has over 20 years of experience providing strategic knowledge management services. If your organization is looking for more advice for cutting-edge solutions to data governance issues, contact us!  

The post Entitlements Within a Semantic Layer Framework: Benefits of Determining User Roles Within a Data Governance Framework appeared first on Enterprise Knowledge.

The ultimate goal of the Knowledge Portal is to provide the right people access to the right information at the right time. This blog focuses on the first part of that statement—“the right people.” This securing of information assets is called entitlements. As our COO Joe Hilger eloquently points out, entitlements are vital in “enabling consistent and correct privileges across every system and asset type in the organization.” The trick is to ensure that an organization’s security model is maintained when aggregating this disparate information into a single view so that users only see what they are supposed to.

The Knowledge Portal Security Challenge

The Knowledge Portal’s core value lies in its ability to aggregate information from multiple source systems into a single application. However, any access permissions established outside of the portal—whether in the source systems or an organization-wide security model—need to be respected. There are many considerations to take into account when doing this. For example, how does the portal know:

• Who am I?
• Am I the same person specified in the various source systems?
• Which information should I be able to see?
• How will my access be removed if my role changes?

Once a user has logged in, the portal needs to know that the user has Role A in the content management system, Role B in our HR system, and Role C in our financial system. Since the portal aggregates information from the aforementioned systems, it uses this information to ensure what I see in the portal is reflective of what I would see in any of the individual systems. 

The Tenets of Unified Entitlements in a Knowledge Portal

At EK, we have a common set of principles that guide us when implementing entitlements for a Knowledge Portal. They include:

• Leveraging a single identity via an Identity Provider (IdP).
• Creating a universal set of groups for access control.
• Respecting access permissions set in source systems when available.
• Developing a security model for systems without access permissions.

Leverage an Identity Provider (IdP)

When I first started working in search over 20 years ago, most source systems had their own user stores—the feature that allows a user to log into a system and uniquely identifies them within the system. One of the biggest challenges for implementing security was correctly mapping a user’s identity in the search application to their various identities in the source systems sending content to the search engine.

Thankfully, enterprise-wide Identity Providers (IdP)  like Okta, Microsoft Entra ID (formerly Azure Active Directory), and Google Cloud Identity are ubiquitous these days.  An Identity Provider (IdP) is like a digital doorkeeper for your organization. It identifies who you are and shares that information with your organization’s applications and systems.

By leveraging an IdP, I can present myself to all my applications with a single identifier such as “cmarino@enterprise-knowledge.com.” For the sake of simplicity in mapping my identity within the Knowledge Portal, I’m not “cmarino” in the content management system, “marinoc” in the HR system, and “christophermarino” in the financial system.

Instead, all of those systems recognize me as “cmarino@enterprise-knowledge.com” including the Knowledge Portal. And the subsequent decision by the portal to provide or deny access to information is greatly simplified. The portal needs to know who I am in all systems to make these determinations.

Create Universal Groups for Access Control

Working hand in hand with an IdP, the establishment of a set of universally used groups for access control is a critical step to enabling Unified Entitlements. These groups are typically created within your IdP and should reflect the common groupings needed to enforce your organization’s security model. For instance,  you might choose to create groups based on a department or a project or a business unit. Most systems provide great flexibility in how these groups are created and managed.

These groups are used for a variety of tasks, such as:

• Associating relevant users to groups so that security decisions are based on a smaller, manageable number of groups rather than on every user in your organization.
• Enabling access to content by mapping appropriate groups to the content.
• Serving as the unifying factor for security decisions when developing an organization’s security model.

As an example, we developed a Knowledge Portal for a large global investment firm which used Microsoft Entra ID as their IdP. Within Entra ID, we created a set of groups based on structures like business units, departments, and organizational roles. Access permissions were applied to content via these groups whether done in the source system or an external security model that we developed. When a user logged in to the portal, we identified them and their group membership and used that in combination with the permissions of the content. Best of all, once they moved off a project or into a different department or role, a simple change to their group membership in the IdP cascaded down to their access permissions in the Knowledge Portal.

Respect Permissions from Source Systems

The first two principles have focused on identifying a user and their roles. However, the second key piece to the entitlements puzzle rests with the content. Most source systems natively provide the functionality to control access to content by setting access permissions. Examples are SharePoint for your organization’s sensitive documents, ServiceNow for tickets only available to a certain group, or Confluence pages only viewable by a specific project team. 

When a security model already exists within a source system, the goal of integrating that content within the Knowledge Portal is simple: respect the permissions established in the source. The key here is syncing your source systems with your IdP and then leveraging the groups managed there. When specifying access to content in the source, use the universal groups. 

Thus, when the Knowledge Portal collects information from the source system, it pulls not only the content and its applicable metadata but also the content’s security information. The permissions are stored alongside the content in the portal’s backend and used to determine whether a specific user can view specific content within the portal. The permissions become just another piece of metadata by which the content can be filtered.

Develop Security Model for Unsupported Systems

Occasionally, there will be source systems where access permissions have not or can not be supported. In this case, you will have to leverage your own internal security model by developing one or using an entitlements tool. Instead of entitlements stored within the source system, the entitlements will be managed through this internal model. 

The steps to accomplish this include:

• Identify the tools needed to support unified entitlements;
• Build the models for applying the security rules; and
• Develop the integrations needed to automate security with other systems. 

The process to implement this within the Knowledge Portal would remain the same: store the access permissions with the content (mapped using groups) and use these as filters to ensure that users see only the information they should.

Conclusion

Getting unified entitlements correct for your organization plays a large part in a successful Knowledge Portal implementation. If you need proven expertise to help guide managing access to your organization’s valuable information, contact us! 

The “right people” in your organization will thank you.

The post Incorporating Unified Entitlements in a Knowledge Portal appeared first on Enterprise Knowledge.

Unified entitlements provide a holistic definition of access rights, enabling consistent and correct privileges across every system and asset type in the organization. These information assets may be documents in SharePoint, wiki pages, discussion threads in Microsoft Teams or Slack, or data sets in a data lake. In most organizations, each of these systems has its own model for securing information. As a result, most organizations have an inconsistent application of their entitlement rules across their knowledge ecosystem. These inconsistencies create risk for the organization and impact the development of knowledge management solutions.

There are several legal, compliance, and reputational risks associated with the inconsistent application of entitlements. In the next few paragraphs, I will share how some of these risks can be realized and how unified entitlements would have fixed the issue.

Automating Access

A consulting firm that we worked with used team sites to collaborate on client materials. The head of the project was allowed to add or remove access to the site. As people left the project, their access was not removed. In addition, a few people from separate divisions were granted access so that they could see deliverables that could help them with their own work. When the client did an audit to see who had access to their materials, they saw several people who were not on the project. The consulting firm was in breach of its contractual obligations, and while it managed to keep the client, it lost the client’s trust and negatively affected its reputation. If they did not have as good of a relationship with their client, they might have been sued. A unified entitlements solution would have synced access to the site with access to the charge code to do the work. As people left the project, they would have automatically lost access to the project workspace, and the client audit would have strengthened the relationship.

Catching Errors

In another example, a manufacturing company had an R&D division that developed plans for new products in their product line. They were in a highly competitive industry where being the first to market could mean millions of dollars in additional revenue. The designs of the new products were stored in a closely guarded Product Lifecycle Management (PLM) system. Access to the PLM was managed by an IT person in the R&D department. An employee with the same name as one of the R&D researchers was accidentally granted access to the PLM. This person did not log into the PLM, but found the upcoming designs in the semantic search that the company rolled out. The person assumed this was public information. They took a job with a competitor and shared what they had seen with their new employer. The competitor copied the approach and closed the gap with the manufacturer. This simple mistake cost the manufacturer millions of dollars in product revenue and directly affected the stock price. In this case, the unified entitlements solution would have granted access to the PLM only to those people in the R&D department. Access permissions would be driven by the department of record in the HR system. All of this would automatically be replicated between systems so that no individual would have the responsibility to grant access to the PLM. 

Aligning Systems

A pharmaceutical company stored data about the results of their drug trials in their data lake. Access to the trial results was limited to people who worked on the clinical trials. In addition, the team captured notes about their interpretations of the results on a separate team site. The person who managed access to the data lake was not the same person who managed access to the team site. One of the tests was executed incorrectly, which led to disappointing results. The cause for the improper results was identified by the team and documented on the team site. New tests were run that produced better results, and the old ones were ignored but left in the data lake. Someone from outside the trials team was accidentally granted access to the test result data but not the notes about the findings. They saw the result data and immediately escalated that there was a problem with their leadership. The clinical trial team spent weeks explaining what they found. This confusion delayed the trial timeline and distracted the team. A unified entitlements solution would have identified the system of record for the trial teams and automatically granted access to both the data tables and the team site based on who was assigned to the trials. 

Conclusion

These three simple examples show the kinds of risks that organizations accept when they do not implement unified entitlements. If your organization has an entitlements problem, it is best to start with a strategy that allows you to understand the scope of the problem and create a reasonable plan for addressing entitlements across the enterprise. Once this is in place, the organization can:

• Identify the products needed to support unified entitlements;
• Start building the models for applying the security rules; and
• Develop the integrations to automate security to all major systems.  

Consistent and accurate access permissions to knowledge assets can no longer be ignored or deprioritized. Semantic solutions like semantic search, knowledge portals, knowledge graphs, and generative AI chatbots make access to information easier than ever before. Organizations that lack the structure of a unified entitlements solution have greater risk of information leakage than ever before. If you need a Unified Entitlements program, our consultants have solved this problem for other large organizations and can help you better understand the problem and help you jump-start your initiative – contact us.

The post Why Your Organization Needs Unified Entitlements appeared first on Enterprise Knowledge.

This blog explores how the integration of secure access management within an enterprise AI framework can transform enterprise AI systems from simple automation tools into secure, context-aware knowledge platforms. We’ll discuss approaches for how modern Role-Based Access Control (RBAC), enhanced by AI capabilities, works to streamline and create a dynamic ecosystem where information flows securely to those who need it most.

Understanding Enterprise AI and Access Control

Enterprise AI represents a significant advancement in how organizations process and utilize their data, moving beyond basic automation to intelligent, context-aware systems. This awareness becomes particularly powerful when combined with sophisticated access management systems. Role-Based Access Control (RBAC) serves as a cornerstone of this integration, providing a framework for regulating access to organizational knowledge based on user roles rather than individual identities. Modern RBAC systems, enhanced by AI, go beyond static permission assignments to create dynamic, context-aware access controls that adapt to organizational needs in real time.

Key Features of AI-Enhanced RBAC

1. Dynamic Role Assignment: AI systems continuously analyze user behavior, responsibilities, and organizational context to suggest and adjust role assignments, ensuring access privileges remain current and appropriate.
2. Intelligent Permission Management: Machine learning algorithms help identify patterns in data usage and access requirements, automatically adjusting permission sets to optimize security while maintaining operational efficiency, thereby upholding the principles of least privilege in the organization.
3. Contextual Access Control: The system considers multiple factors including time, location, device type, and user behavior patterns to make real-time access decisions.
4. Automated Compliance Monitoring: AI-powered monitoring systems track access patterns and flag potential security risks or compliance issues, enabling proactive risk management.

This integration of enterprise AI and RBAC creates a sophisticated framework where access controls become more than just security measures – they become enablers of knowledge flow within the organization.

Secure Access Management for Enterprise AI

Integrating access management with enterprise AI creates a foundation for secure, intelligent knowledge sharing by effectively capturing and utilizing organizational expertise.

Modern enterprises require a thoughtful approach to incorporating domain expertise into AI processes while maintaining strict security protocols. This integration is particularly crucial where domain experts transform their tacit knowledge into explicit, actionable frameworks that can enhance AI system capabilities. The AI-RBAC framework embodies this principle through two key components that work in harmony:

1. Adaptable Rule Foundation (ARF) for systematic content classification
2. Expert-driven Organizational Role Mapping for secure knowledge sharing

While ARF provides the structure for explicit knowledge through content tagging, the role mapping performed by Subject Matter Experts (SMEs) injects critical domain intelligence into the organizational knowledge framework, creating a robust foundation for secure knowledge sharing. The ARF system exemplifies this integration by classifying and managing data across three distinct levels, while SMEs provide the crucial expertise needed to map these classifications to organizational roles. This combination ensures that organizational knowledge is not only properly categorized but also securely accessible to the right people at the right time, effectively bridging the gap between AI-driven classification and human expertise.

The Adaptable Rule Foundation (ARF) system exemplifies this integration by classifying and managing data across three distinct levels:

• Core Level: Includes fundamental organizational knowledge and critical business rules, defined with input from domain SMEs.
• Common Level: Contains shared knowledge assets and cross-departmental information, with SME guidance on scope.
• Unique Level: Manages specialized knowledge specific to individual departments or projects, as defined by SMEs.

SMEs play a crucial role in adjusting the scope and definitions of the Core, Common, and Unique levels to inject their domain expertise into the ARF framework. This ensures the classification system aligns with real-world organizational knowledge and needs.

This three-tiered approach, powered by AI, enables organizations to:

• Automatically classify incoming data based on sensitivity and relevance
• Dynamically apply appropriate access controls using expert-driven organizational role mapping
• Enable domain experts to contribute knowledge securely without requiring technical expertise
• Adapt security measures in real-time based on organizational changes

The ARF system’s intelligence goes beyond traditional access management by understanding not just who should access information, but how that information fits into the broader organizational knowledge ecosystem. This contextual awareness ensures that security measures enhance, rather than hinder, knowledge sharing.

The Future of Enterprise AI

As organizations continue to leverage AI capabilities, the interaction between access management and enterprise AI becomes increasingly crucial. This integration ensures that AI systems serve as secure, intelligent platforms for knowledge sharing and decision-making. The combination of dynamic access controls and enterprise AI framework creates an environment where:

• Security becomes an enabler rather than a barrier to innovation
• Domain expertise naturally flows into AI systems through secure channels
• Organizations can adapt quickly to changing knowledge needs while maintaining security
• AI systems become more contextually aware and organizationally aligned

If your organization is looking to enhance AI capabilities while ensuring robust data security, our enterprise AI access management framework offers a powerful solution. Contact us to learn how to transform your organization’s knowledge infrastructure into a secure, intelligent ecosystem that drives innovation and growth.

The post Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI appeared first on Enterprise Knowledge.