One key step in preparing content is the application of metadata and organizational context to pieces of content through tagging. There are several tagging approaches an organization can take to enrich pre-existing content with metadata and organizational context, including manual tagging, automated tagging capabilities from a taxonomy and ontology management system (TOMS), using apps and features directly from a content management solution, and various hybrid approaches. While many of these approaches, in particular acquiring a TOMS, are recommended as a long-term auto-tagging solution, EK has recommended and implemented Large Language Model (LLM)-based auto-tagging capabilities across several recent engagements. Due to LLM-based tagging’s lower initial investment compared to a TOMS and its greater efficiency than manual tagging, these auto-tagging solutions have been able to provide immediate value and jumpstart the process of re-tagging existing content. This blog will dive deeper into how LLM tagging works, the value of semantics, technical considerations, and next steps for implementing an LLM-based tagging solution.

Overview of LLM-Based Auto-Tagging Process

Similar to existing auto-tagging approaches, the LLM suggests a tag by parsing through a piece of content, processing and identifying key phrases, terms, or structure that gives the document context. Through prompt engineering, the LLM is then asked to compare the similarity of key semantic components (e.g., named entities, key phrases) with various term lists, returning a set of terms that could be used to categorize the piece of content. These responses can be adjusted in the tagging workflow to only return terms meeting a specific similarity score. These tagging results are then exported to a data store and applied to the content source. Many factors, including the particular LLM used, the knowledge an LLM is working with, and the source location of content, can greatly impact the tagging effectiveness and accuracy. In addition, adjusting parameters, taxonomies/term lists, and/or prompts to improve precision and recall can ensure tagging results align with an organization’s needs. The final step is the auto-tagging itself and the application of the tags in the source system. This could look like a script or workflow that applies the stored tags to pieces of content.

Figure 1: High-level steps for LLM content enrichment

EK has put these steps into practice, for example, when engaging with a trade association on a content modernization project to migrate and auto-tag content into a new content management system (CMS). The organization had been struggling with content findability, standardization, and governance, in particular, the language used to describe the diverse areas of work the trade association covers. As part of this engagement, EK first worked with the organization’s subject matter experts (SMEs) to develop new enterprise-wide taxonomies and controlled vocabularies integrated across multiple platforms to be utilized by both external and internal end-users. To operationalize and apply these common vocabularies, EK developed an LLM-based auto-tagging workflow utilizing the four high-level steps above to auto-tag metadata fields and identify content types. This content modernization effort set up the organization for document workflows, search solutions, and generative AI projects, all of which are able to leverage the added metadata on documents. 

Value of Semantics with LLM-Based Auto-Tagging

Semantic models such as taxonomies, metadata models, ontologies, and content types can all be valuable inputs to guide an LLM on how to effectively categorize a piece of content. When considering how an LLM is trained for auto-tagging content, a greater emphasis needs to be put on organization-specific context. If using a taxonomy as a training input, organizational context can be added through weighting specific terms, increasing the number of synonyms/alternative labels, and providing organization-specific definitions. For example, by providing organizational context through a taxonomy or business glossary that the term “Green Account” refers to accounts that have met a specific environmental standard, the LLM would not accidentally tag content related to the color green or an account that is financially successful.

Another benefit of an LLM-based approach is the ability to evolve both the semantic model and LLM as tagging results are received. As sets of tags are generated for an initial set of content, the taxonomies and content models being used to train the LLM can be refined to better fit the specific organizational context. This could look like adding additional alternative labels, adjusting the definition of terms, or adjusting the taxonomy hierarchy. Similarly, additional tools and techniques, such as weighting and prompt engineering, can tune the results provided by the LLM and evolve the results generated to achieve a higher recall (rate the LLM is including the correct term) and precision (rate the LLM is selecting only the correct term) when recommending terms. One example of this is  adding weighting from 0 to 10 for all taxonomy terms and assigning a higher score for terms the organization prefers to use. The workflow developed alongside the LLM can use this context to include or exclude a particular term.

Implementation Considerations for LLM-Based Auto-Tagging 

Several factors, such as the timeframe, volume of information, necessary accuracy, types of content management systems, and desired capabilities, inform the complexity and resources needed for LLM-based content enrichment. The following considerations expand upon the factors an organization must consider for effective LLM content enrichment. 

Tagging Accuracy

The accuracy of tags from an LLM directly impacts end-users and systems (e.g., search instances or dashboards) that are utilizing the tags. Safeguards need to be implemented to ensure end-users can trust the accuracy of the tagged content they are using. These help ensure that a user is not mistakenly accessing or using a particular document, or that they are frustrated by the results they get. To mitigate both of these concerns, a high recall and precision score with the LLM tagging improves the overall accuracy and lowers the chance for miscategorization. This can be done by investing further into human test-tagging and input from SMEs to create a gold-standard set of tagged content as training data for the LLM. The gold-standard set can then be used to adjust how the LLM weights or prioritizes terms, based on the organizational context in the gold-standard set. These practices will help to avoid hallucinations (factually incorrect or misleading content) that could appear in applications utilizing the auto-tagged set of content.

Content Repositories

One factor that greatly adds technical complexity is accessing the various types of content repositories that an LLM solution, or any auto-tagging solution, needs to read from. The best content management practice for auto-tagging is to read content in its source location, limiting the risk of duplication and the effort needed to download and then read content. When developing a custom solution, each content repository often needs a distinctive approach to read and apply tags. A content or document repository like SharePoint, for example, has a robust API for reading content and seamlessly applying tags, while a less widely adopted platform may not have the same level of support. It is important to account for the unique needs of each system in order to limit the disruption end-users may experience when embarking on a tagging effort.

Knowledge Assets

When considering the scalability of the auto-tagging effort, it is also important to evaluate the breadth of knowledge asset types being analyzed. While the ability of LLMs to process several types of knowledge assets has been growing, each step of additional complexity, particularly evaluating multiple types, can result in additional resources and time needed to read and tag documents. A PDF document with 2-3 pages of content will take far fewer tokens and resources for an LLM to read its content than a long visual or audio asset. Going from a tagging workflow of structured knowledge assets to tagging unstructured content will increase the overall time, resources, and custom development needed to run a tagging workflow. 

Data Security & Entitlements

When utilizing an LLM, it is recommended that an organization invest in a private or an in-house LLM to complete analysis, rather than leveraging a publicly available model. In particular, an LLM does not need to be ‘on-premises’, as several providers have options for LLMs in your company’s own environment. This ensures a higher level of document security and additional features for customization. Particularly when tackling use cases with higher levels of personal information and access controls, a robust mapping of content and an understanding of what needs to be tagged is imperative. As an example, if a publicly facing LLM was reading confidential documents on how to develop a company-specific product, this information could then be leveraged in other public queries and has a higher likelihood of being accessed outside of the organization. In an enterprise data ecosystem, running an LLM-based auto-tagging solution can raise red flags around data access, controls, and compliance. These challenges can be addressed through a Unified Entitlements System (UES) that creates a centralized policy management system for both end users and LLM solutions being deployed.

Next Steps:

One major consideration with an LLM tagging solution is maintenance and governance over time. For some organizations, after completing an initial enrichment of content by the LLM, a combination of manual tagging and forms within each CMS helps them maintain tagging standards over time. However, a more mature organization that is dealing with several content repositories and systems may want to either operationalize the content enrichment solution for continued use or invest in a TOMS. With either approach, completing an initial LLM enrichment of content is a key method to prove the value of semantics and metadata to decision-makers in an organization. 
Many technical solutions and initiatives that excite both technical and business stakeholders can be actualized by an LLM content enrichment effort. By having content that is tagged and adhering to semantic standards, solutions like knowledge graphs, knowledge portals, and semantic search engines, or even an enterprise-wide LLM Solution, are upgraded even further to show organizational value.

If your organization is interested in upgrading your content and developing new KM solutions, contact us!

The post How to Leverage LLMs for Auto-tagging & Content Enrichment appeared first on Enterprise Knowledge.

Though the world is enthralled with the myriad of public AI solutions, many organizations struggle to make the leap to reliable AI within their organizations. A recent MIT report, “The GenAI Divide,” reveals a concerning truth: despite significant investments in AI, 95% of organizations are not seeing any benefits from their AI investments. 

One of the core impediments to achieving AI within your own organization is poor-quality content and data. Without the proper foundation of high-quality content and data, any AI solution will be rife with ‘hallucinations’ and errors. This will expose organizations to unacceptable risks, as AI tools may deliver incorrect or outdated information, leading to dangerous and costly outcomes. This is also why tools that perform well as demos fail to make the jump to production.  Even the most advanced AI won’t deliver acceptable results if an organization has not prepared their content and data.

This blog outlines seven top ways to ensure your content and data are AI-ready. With the right preparation and investment, your organization can successfully implement the latest AI technologies and deliver trustworthy, complete results.

1) Understand What You Mean by “Content” and/or “Data” (Knowledge Asset Definition)

While it seems obvious, the first step to ensuring your content and data are AI-ready is to clearly define what “content” and “data” mean within your organization. Many organizations use these terms interchangeably, while others use one as a parent term of the other. This obviously leads to a great deal of confusion. 

Leveraging the traditional definitions, we define content as unstructured information (ranging from files and documents to blocks of intranet text), and data as structured information (namely the rows and columns in databases and other applications like Customer Relationship Management systems, People Management systems, and Product Information Management systems). You are wasting the potential of AI if you’re not seeking to apply your AI to both content and data, giving end users complete and comprehensive information. In fact, we encourage organizations to think even more broadly, going beyond just content and data to consider all the organizational assets that can be leveraged by AI.

We’ve coined the term knowledge assets to express this. Knowledge assets comprise all the information and expertise an organization can use to create value. This includes not only content and data, but also the expertise of employees, business processes, facilities, equipment, and products. This manner of thinking quickly breaks down artificial silos within organizations, getting you to consider your assets collectively, rather than by type. Moving forward in this article, we’ll use the term knowledge assets in lieu of content and data to reinforce this point. Put simply and directly, each of the below steps to getting your content and data AI-ready should be considered from an enterprise perspective of knowledge assets, so rather than discretely developing content governance and data governance, you should define a comprehensive approach to knowledge asset governance. This approach will not only help you achieve AI-readiness, it will also help your organization to remove silos and redundancies in order to maximize enterprise efficiency and alignment.

2) Ensure Quality (Asset Cleanup)

We’ve found that most organizations are maintaining approximately 60-80% more information than they should, and in many cases, may not even be aware of what they still have. That means that four out of five knowledge assets are old, outdated, duplicate, or near-duplicate. 

There are many costs to this over-retention before even considering AI, including the administrative burden of maintaining this 80% (including the cost and environmental impact of unnecessary server storage), and the usability and findability cost to the organization’s end users when they go through obsolete knowledge assets.

The AI cost becomes even higher for several reasons. First, AI typically “white labels” the knowledge assets it finds. If a human were to find an old and outdated policy, they may recognize the old corporate branding on it, or note the date from several years ago on it, but when AI leverages the information within that knowledge asset and resurfaces it, it looks new and the contextual clues are lost.

Next, we have to consider the old adage of “garbage in, garbage out.” Incorrect knowledge assets fed to an AI tool will result in incorrect results, also known as hallucinations. While prompt engineering can be used to try to avoid these conflicts and, potentially even errors, the only surefire guarantee to avoid this issue is to ensure the accuracy of the original knowledge assets, or at least the vast majority of it.

Many AI models also struggle with near-duplicate “knowledge assets,” unable to discern which version is trusted. Consider your organization’s version control issues, working documents, data modeled with different assumptions, and iterations of large deliverables and reports that are all currently stored. Knowledge assets may go through countless iterations, and most of the time, all of these versions are saved. When ingested by AI, multiple versions present potential confusion and conflict, especially when these versions didn’t simply build on each other but were edited to improve findings or recommendations. Each of these, in every case, is an opportunity for AI to fail your organization.

Finally, this would also be the point at which you consider restructuring your assets for improved readability (both by humans and machines). This could include formatting (to lower cognitive lift and improve consistency) from a human perspective. For both humans and AI, this could also mean adding text and tags to better describe images and other non-text-based elements. From an AI perspective, in longer and more complex assets, proximity and order can have a negative impact on precision, so this could include restructuring documents to make them more linear, chronological, or topically aligned. This is not necessary or even important for all types of assets, but remains an important consideration especially for text-based and longer types of assets.

3) Fill Gaps (Tacit Knowledge Capture)

The next step to ensure AI readiness is to identify your gaps. At this point, you should be looking at your AI use cases and considering the questions you want AI to answer. In many cases, your current repositories of knowledge assets will not have all of the information necessary to answer those questions completely, especially in a structured, machine-readable format. This presents a risk itself, especially if the AI solution is unaware that it lacks the complete range of knowledge assets necessary and portrays incomplete or limited answers as definitive. 

Filling gaps in knowledge assets is extremely difficult. The first step is to identify what is missing. To invoke another old adage, organizations have long worried they “don’t know what they don’t know,” meaning they lack the organizational maturity to identify gaps in their own knowledge. This becomes a major challenge when proactively seeking to arm an AI solution with all the knowledge assets necessary to deliver complete and accurate answers. The good news, however, is that the process of getting knowledge assets AI-ready helps to identify gaps. In the next two sections, we cover semantic design and tagging. These steps, among others, can identify where there appears to be missing knowledge assets. In addition, given the iterative nature of designing and deploying AI solutions, the inability of AI to answer a question can trigger gap filling, as we cover later. 

Of course, once you’ve identified the gaps, the real challenge begins, in that the organization must then generate new knowledge assets (or locate “hidden” assets) to fill those gaps. There are many techniques for this, ranging from tacit knowledge capture, to content inventories, all of which collectively can help an organization move from AI to Knowledge Intelligence (KI).    

4) Add Structure and Context (Semantic Components)

Once the knowledge assets have been cleansed and gaps have been filled, the next step in the process is to structure them so that they can be related to each other correctly, with the appropriate context and meaning. This requires the use of semantic components, specifically, taxonomies and ontologies. Taxonomies deliver meaning and structure, helping AI to understand queries from users, relate knowledge assets based on the relationships between the words and phrases used within them, and leverage context to properly interpret synonyms and other “close” terms. Taxonomies can also house glossaries that further define words and phrases that AI can leverage in the generation of results.

Though often confused or conflated with taxonomies, ontologies deliver a much more advanced type of knowledge organization, which is both complementary to taxonomies and unique. Ontologies focus on defining relationships between knowledge assets and the systems that house them, enabling AI to make inferences. For instance:

<Person> works at <Company>

<Zach Wahl> works at <Enterprise Knowledge>

<Company> is expert in <Topic>

<Enterprise Knowledge> is expert in <AI Readiness>

From this, a simple inference based on structured logic can be made, which is that the person who works at the company is an expert in the topic: Zach Wahl is an expert in AI Readiness. More detailed ontologies can quickly fuel more complex inferences, allowing an organization’s AI solutions to connect disparate knowledge assets within an organization. In this way, ontologies enable AI solutions to traverse knowledge assets, more accurately make “assumptions,” and deliver more complete and cohesive answers. 

Collectively, you can consider these semantic components as an organizational map of what it does, who does it, and how. Semantic components can show an AI how to get where you want it to go without getting lost or taking wrong turns.

5) Semantic Model Application (Tagging)

Of course, it is not sufficient simply to design the semantic components; you must complete the process by applying them to your knowledge assets. If the semantic components are the map, applying semantic components as metadata is the GPS that allows you to use it easily and intuitively. This step is commonly a stumbling block for organizations, and again is why we are discussing knowledge assets rather than discrete areas like content and data. To best achieve AI readiness, all of your knowledge assets, regardless of their state (structured, unstructured, semi-structured, etc), must have consistent metadata applied against them. 

When applied properly, this consistent metadata becomes an additional layer of meaning and context for AI to leverage in pursuit of complete and correct answers. With the latest updates to leading taxonomy and ontology management systems, the process of automatically applying metadata or storing relationships between knowledge assets in metadata graphs is vastly improved, though still requires a human in the loop to ensure accuracy. Even so, what used to be a major hurdle in metadata application initiatives is much simpler than it used to be.

6) Address Access and Security (Unified Entitlements)

What happens when you finally deliver what your organization has been seeking, and give it the ability to collectively and completely serve their end users the knowledge assets they’ve been seeking? If this step is skipped, the answer is calamity. One of the express points of the value of AI is that it can uncover hidden gems in knowledge assets, make connections humans typically can’t, and combine disparate sources to build new knowledge assets and new answers within them. This is incredibly exciting, but also presents a massive organizational risk.

At present, many organizations have an incomplete or actually poor model for entitlements, or ensuring the right people see the right assets, and the wrong people do not. We consistently discover highly sensitive knowledge assets in various forms on organizational systems that should be secured but are not. Some of this takes the form of a discrete document, or a row of data in an application, which is surprisingly common but relatively easy to address. Even more of it is only visible when you take an enterprise view of an organization. 

For instance, Database A might contain anonymized health information about employees for insurance reporting purposes but maps to discrete unique identifiers. File B includes a table of those unique identifiers mapped against employee demographics. Application C houses the actual employee names and titles for the organizational chart, but also includes their unique identifier as a hidden field. The vast majority of humans would never find this connection, but AI is designed to do so and will unabashedly generate a massive lawsuit for your organization if you’re not careful.

If you have security and entitlement issues with your existing systems (and trust me, you do), AI will inadvertently discover them, connect the dots, and surface knowledge assets and connections between them that could be truly calamitous for your organization. Any AI readiness effort must confront this challenge, before your AI solutions shine a light on your existing security and entitlements issues.

7) Maintain Quality While Iteratively Improving (Governance)

Steps one through six describe how to get your knowledge assets ready for AI, but the final step gets your organization ready for AI. With a massive investment in both getting your knowledge assets in the right state for AI and in  the AI solution itself, the final step is to ensure ongoing quality of both. Mature organizations will invest in a core team to ensure knowledge assets go from AI-ready to AI-mature, including:

• Maintaining and enforcing the core tenets to ensure knowledge assets stay up-to-date and AI solutions are looking at trusted assets only;
• Reacting to hallucinations and unanswerable questions to fill gaps in knowledge assets; 
• Tuning the semantic components to stay up to date with organizational changes.

The most mature organizations, those wishing to become AI-Powered organizations, will look first to their knowledge assets as the key building block to drive success. Those organizations will seek ROCK (Relevant, Organizationally Contextualized, Complete, and Knowledge-Centric) knowledge assets as the first line to delivering Enterprise AI that can be truly transformative for the organization. 

If you’re seeking help to ensure your knowledge assets are AI-Ready, contact us at info@enterprise-knowledge.com. 

The post Top Ways to Get Your Content and Data Ready for AI 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!  

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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.

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.

The field of Knowledge Management continues to experience a period of rapid evolution, and with it, growing opportunity to redefine value and reorient decision-makers and stakeholders toward the business value the field offers. With the nature of work continuing to evolve in a post-Covid world, the “AI Revolution” dominating conversations and instances of Generative AI seemingly everywhere, and the field of Knowledge, Information, Data, and Content Management continuing to connect in new ways, Knowledge Management continues to evolve. 

As in years past, my annual report on Top Knowledge Management Trends for 2025 is based on an array of factors and inputs. As the largest global KM consultancy, EK is in a unique position to identify where KM is and where it is heading. Along with my colleagues, I interview clients and map their priorities, concerns, and roadmaps. We also sample the broad array of requests and inquiries we receive from potential clients and analyze various requests for proposal and information (RFPs and RFIs). In addition, we attend conferences not just for KM, both more broadly across industries and related fields to understand where the “buzz” is. I then supplement these and other inputs with interviews from leaders in the field and inputs from EK’s Expert Advisory Board (EAB). From that, I identify what I see as the top trends in KM.

You can review each of these annual blogs for 2024, 2023, 2022, 2021, 2020, and 2019 to get a sense of how the world of KM has rapidly progressed and to test my own track record. Now, here’s the list of the Top Knowledge Management trends for 2025.

1) AI-KM Symbiosis – Everyone is talking about AI and we’re seeing massive budgets allocated to make it a reality for organizations, rather than simply something that demonstrates well but generates too many errors to be trusted. Meanwhile, many KM practitioners have been asking what their role in the world of AI will be. In last year’s KM Trends blog I established the simple idea that AI can be used to automate and simplify otherwise difficult and time-consuming aspects of KM programs, and equally, KM design and governance practices can play a major role in making AI “work” within organizations. I doubled down on this idea during my keynote at last year’s Knowledge Summit Dublin, where I presented the two sides of the coin, KM for AI, and AI for KM, and more recently detailed this in a blog while introducing the term Knowledge Intelligence (KI).

In total, this can be considered as the mutually beneficial relationship between Artificial Intelligence and Knowledge Management, which all KM professionals should be seizing upon to help organizations understand and maximize their value, and for which the broader community is quickly becoming aware. Core KM practices and design frameworks address many of the reliability, completeness, and accuracy issues organizations are reporting with AI – for instance, taxonomy and ontology to enable context and categorization for AI, tacit knowledge capture and expert identification to deliver rich knowledge assets for AI to leverage, and governance to ensure the answers are correct and current. 

AI, on the other hand, delivers inference, assembly, delivery, and machine learning to speed up and automate otherwise time intensive human-based tasks that were rife with inconsistencies. AI can help to deliver the right knowledge to the right people at the moment of need through automation and inference, it can automate tasks like tagging, and even improve tacit knowledge capture, which I cover below in greater detail as a unique trend.

2) AI-Ready Content – Zeroing in on one of the greatest gaps in high-performing AI systems, a key role for KM professionals this year will be to establish and guide the processes and organizational structures necessary to ensure content ingested by an organization’s AI systems is connectable and understandable, accurate, up-to-date, reliable, and eminently trusted. There are several layers to this, in all of which Knowledge Management professionals should play a central role. First is the accuracy and alignment of the content itself. Whether we’re talking structured or unstructured, one of the greatest challenges organizations face is the maintenance of their content. This has been a problem long before AI, but it is now compounded by the fact that an AI system can connect with a great deal of content and repackage it in a way that potentially looks new and more official than the source content. What happens when an AI system is answering questions based on an old directive, outdated regulation, or even completely wrong content? What does it do if it finds multiple conflicting pieces of information? This is where “hallucinations” start appearing, with people quickly losing trust in AI solutions.

In addition to the issues of quality and reliability, there are also content issues related to structure and state. AI solutions perform better when content in all forms has been tagged consistently with metadata and certain systems and use cases benefit from consistent structure and state of content as well. For organizations that have previously invested in their information and data practices, leveraging taxonomies, ontologies, and other information definition and categorization solutions, trusted AI solutions will be a closer reality. For the many others, this must be an area of focus.

Notably, we’ve even seen a growing number of data management experts making a call for greater Knowledge Management practices and principles in their own discipline. The world is waking up to the value of KM. In 2025, there will be a growing priority on this age-old problem of getting an organization’s content, and content governance, in order so that those materials surfaced through AI will be consistently trusted and actionable.

3) Filling Knowledge Gaps – All systems, AI-driven or otherwise, are only as smart as the knowledge they can ingest. As systems leverage AI more and transcend individual silos to operate for the entire enterprise, there’s a great opportunity to better understand what people are asking for. This goes beyond analytics, though that is a part of it, but rather focuses on an understanding of what was asked that couldn’t be answered. Once enterprise-level knowledge assets are united, these AI and Semantic Layer solutions have the ability to identify knowledge gaps. 

This creates a massive opportunity for Knowledge Management professionals. A key role of KM professionals has always been to proactively fill these knowledge gaps, but in so many organizations, simply knowing what you don’t know is a massive feat in itself. As systems converge and connect, however, organizations will suddenly have an ability to spot their knowledge gaps as well as their potential “single points of failure,” where only a handful of experts possess critical knowledge within the organization. This new map of knowledge flows and gaps can be a tool for KM professionals to prioritize filling the most critical gaps and track their progress for the organization. This in turn can create an important new ability for KM professionals to demonstrate their value and impact for organizations, showing how previously unanswerable questions are now addressed and how past single points of failure no longer exist. 

To paint the picture of how this works, imagine a united organization that could receive regular, automated reports on the topics for which people were seeking answers but the system was unable to provide. The organization could then prioritize capturing tacit knowledge, fostering new communities of practice, generating new documentation, and building new training around those topics. For instance, if a manufacturing company had a notable spike in user queries about a particular piece of equipment, the system would be able to notify the KM professionals, allowing them to assess why this was occurring and begin creating or curating knowledge to better address those queries. The most intelligent systems would be able to go beyond content and even recognize when an organization’s experts on a particular topic were dwindling to the point that a future knowledge gap might exist, alerting the organization to enhance knowledge capture, hiring, or training. 

4) AI-Assisted Tacit Knowledge Capture – Since the late 1990’s, I’ve seen people in the KM field seek to automate the process of tacit knowledge capture. Despite many demos and good ideas over the decades, I’ve never found a technical solution that approximates a human-driven knowledge capture approach. I believe that will change in the coming years, but for now the trend isn’t automated knowledge capture, it is AI-assisted knowledge capture. There’s a role for both KM professionals and AI solutions to play in this approach. The human’s responsibilities are to identify high value moments of knowledge capture, understand who holds that knowledge and what specifically we want to be able to answer (and for whom), and then facilitate the conversations and connect to have that knowledge transferred to others. 

That’s not new, but it is now scalable and easier to digitize when AI and automation are brought into the processes. The role of the AI solution is to record and transcribe the capture and transfer of knowledge, automatically ingesting the new assets into digital form, and then leveraging it as part of the new AI body of knowledge to serve up to others at the point of need. By again considering the partnership between Knowledge Management professionals and the new AI tools that exist, practices and concepts that were once highly limited to human interactions can be multiplied and scaled to the enterprise, allowing the KM professional to do more that leverages their expertise, and automating the drudgery and low-impact tasks.

5) Enterprise Semantic Layers – Last year in this KM Trends blog, I introduced the concept of the Semantic Layer. I identified it as the next step for organizations seeking enterprise knowledge capabilities beyond the maturity of knowledge graphs, as a foundational framework that can make AI a reality for your organization. Over the last year we saw that term enter firmly into the conversation and begin to move into production for many large organizations. That trend is already continuing and growing in 2025. In 2025, organizations will move from prototyping and piloting semantic layers to putting them into production. The most mature organizations will leverage their semantic layers for multiple different front-end solutions, including AI-assisted search, intelligent chatbots, recommendation engines, and more.

6) Access and Entitlements – So what happens when, through a combination of semantic layers, enterprise AI, and improved knowledge management practices an organization actually achieves what they’ve been seeking and connects knowledge assets of all different types, spread across the enterprise in different systems, and representing different eras of the organization? The potential is phenomenal, but there is also a major risk. Many organizations struggle mightily with the appropriate access and entitlements to their knowledge assets. Legacy file drives and older systems possess dark content and data that should be secured but isn’t. This largely goes unnoticed when those materials are “hidden” by poor findability and confused information architectures. All of a sudden, as those issues melt away thanks to AI and semantic layers, knowledge assets that should be secured will be exposed. Though not specifically a knowledge management problem, the work of knowledge managers and others within organizations to break down silos, connect content in context, and improve enterprise findability and discoverability will surface this security and access issue. It will need to be addressed proactively lest organizations find themselves exposing materials they shouldn’t. 

I anticipate this will be a hard lesson learned for many organizations in 2025. As they succeed in the initial phases of production AI and semantic layer efforts, there will be unfortunate exposures. Rather than delivering the right knowledge to the right people, the wrong knowledge will be delivered to the wrong people. The potential risk and impact for this is profound. It will require KM professionals to help identify this risk, not solve it independently, but partner with others in an organization to recognize it and plan to avoid it.

7) More Specific Use Cases (and Harder ROI) – In 2024, we heard a lot of organizations saying “we want AI,” “we need a semantic layer,” or “we want to automate our information processes.” As these solutions become more real and organizations become more educated about the “how” and “why,” we’ll see growing maturity around these requests. Rather than broad statements about technology and associated frameworks, we’ll see more organizations formulating cohesive use cases and speaking more in terms of outcomes and value. This will help to move these initiatives from interesting nice-to-have experiments to recession-proof, business critical solutions. The knowledge management professionals’ responsibility is to guide these conversations. Zero your organization in on the “why?” and ensure you can connect the solution and framework to the specific business problems they will solve, and then to the measurable value they will deliver for the organization.

Knowledge Management professionals are poised to play a major role in these new KM Trends. Many of them, as you read above, pull on long-standing KM responsibilities and skills, ranging from tacit knowledge capture, to taxonomy and ontology design, as well as governance and organizational design. The most successful KM’ers in 2025 will be those that merge these traditional skillsets with a deeper understanding of semantics and their associated technologies, continuing to connect the fields of Knowledge, Content, Information, and Data Management as the connectors and silo busters for organizations.

Where does your organization currently stand with each of these trends? Are you in a position to ensure you’re at the center of these solutions for your organization, leading the way and ensuring knowledge assets are connected and delivered with high-value and high-reliability context? Contact us to learn more and get started.

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