• 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.

This is Part 1 of a two-part series on graph database PoC success and production deployment.

Introduction

I began my journey with graphs around 2014 when I discovered network theory and tools like NetworkX and Neo4j. As our world becomes increasingly connected, it makes sense to work with data by leveraging its inherent connections. Soon, every problem I faced seemed ideally suited for graph solutions.

Early in my experiences, I worked with a biotech startup, exploring how graphs could surface insights into drug-protein interactions (DPI). The team was excited about graphs’ potential to reveal latent signals that traditional analytics missed. With a small budget, we created a Proof-of-Concept (PoC) to demonstrate the “art of the possible.” After a quick kick-off meeting, we loaded data into a free graph database and wrote queries exploring the DPI network. In just three months, we established novel insights that advanced the team’s understanding.

Despite what we considered success, the engagement wasn’t extended. More troubling, I later learned our PoC had been put into a production-like environment where it failed to scale in performance or handle new data sources. What went wrong? How had we lost the potential scientific value of what we’d built?

This experience highlights a common problem in the graph domain: many promising PoCs never make it to production. Through reflection, I’ve developed strategies for avoiding these issues and increasing the likelihood of successful transitions to production. This blog explores why graph PoCs fail and presents a holistic approach for success. It complements the blog Why Graph Implementations Fail (Early Signs & Successes).

Why Graph Database Solutions and Knowledge Graph PoCs Often Fail

Organizational Challenges

Lack of Executive Sponsorship and Alignment

Successful production deployments require strong top-level support. Without executive buy-in, graph initiatives seldom become priorities or receive funding. Executives often don’t understand the limitations of existing approaches or the paradigm shift that graphs represent.

The lack of sponsorship is compounded by how graph practitioners approach stakeholders. We often begin with technical explanations of graph theory, ontologies, and the differences between Resource Description Framework (RDF) and Label Property Graphs (LPG), rather than focusing on business value. No wonder executives struggle to understand why graph initiatives deserve funding over other projects. I’ve been guilty of this myself, starting conversations with “Let me tell you about Leonhard Euler and graph theory…” instead of addressing business problems directly.

Middle Management Resistance and Data Silos

Even with executive interest, mid-level managers can inhibit progress. Many have vested interests in existing systems and fear losing control over their data domains. They’re comfortable with familiar relational databases and may view knowledge graphs as threats to their “systems of record”. This presents an opportunity to engage managers and demonstrate how graphs can integrate with existing systems and support their goals.For example, a graph database may load data “just in time” to perform a connected data analysis and then drop the data after returning the analytic results. This would be an ephemeral use of graph analytics.

Bureaucracy and Data Duplication Concerns

Large organizations have lengthy approval processes for new technologies. Infrastructure teams may be reluctant to support experimental technology without an established return on investment  (ROI).

A critical but often undiscussed factor is that graph databases typically require extracting data from existing sources and creating another copy—raising security risks, infrastructure costs, and data synchronization concerns. This is the Achilles heel of graph databases. However, emerging trends in decoupling data from query engines may offer alternatives to this problem. A new paradigm is emerging where data in data lakes can be analyzed through a graph lens at rest without an ETL ingestion into a graph database. Graph query engines enable data to be viewed through traditional relational and now connected data lenses.

Isolated Use Cases and Limited Understanding

Many graph initiatives start as isolated projects tackling narrow use cases. While this limits upfront risk, it can make the impact seem trivial. Conventional technologies might solve that single problem adequately, leading skeptics to question whether a new approach is needed. The real value of knowledge graphs emerges when connecting data across silos—something that’s hard to demonstrate in limited-scope PoCs.

A practical approach I’ve found effective is asking stakeholders to diagram their problem at a whiteboard. This naturally reveals how they’re already thinking in graph terms, making it easier to demonstrate the value of a graph approach.

Talent and Skills Gap

Graph technologies require specialized skills that are in short supply. Learning curve issues affect even experienced developers, who must master new query languages and paradigms. This shortage of expertise can lead to reliance on a few key individuals, putting projects at risk if they leave.

Technical Challenges

Complex Data Modeling

Graph data models require a different mindset than relational schemas. Designing an effective graph schema or ontology is complex, and mistakes can lead to poor performance. Equally, an effective semantic layer is critical to understanding the meaning of an organization’s data. The schema-less flexibility of graphs can be a double-edged sword—without careful planning, a PoC might be built ad-hoc and prove inefficient or lead to data quality issues when scaled up. Refactoring a graph model late in development can be a major undertaking that casts doubt on the technology itself.

Integration Challenges

Enterprise data rarely lives in one place. Integrating graphs and other solutions with legacy systems requires extensive data mapping and transformation. Without smooth interoperability via connectors, APIs, or virtualization layers, the graph becomes an isolated silo with limited production value. Decoupled approaches mentioned above address this solution by focusing on graph and connected data analytics as a standalone feature of graph query engines. Tooling optimized for graphs are making ETL and integration of graph databases easier and more efficient.

Performance Trade-offs

Graph databases excel at traversing complex relationships but may underperform for simple transactions compared to optimized relational databases. In a PoC with a small dataset, this may not be immediately noticeable, but production workloads expose these limitations. As data volumes grow, even traversals that were fast in the PoC can slow significantly, requiring careful performance tuning and possibly hybrid approaches.

Evolving Standards and Tooling

The graph ecosystem is still evolving, with multiple database models and query languages (Cypher, Gremlin, SPARQL). More recently, decoupled graph query engines enable analyzing tabular and columnar data as if it were a graph, supporting the concept of “Single Copy Analytics” and concurrently increasing the breadth of options for graph analytics. Unlike the relational world with SQL and decades of tooling, graph technologies lack standardization, making it difficult to find mature tools for monitoring, validation, and analytics integration. This inconsistency means organizations must develop more in-house expertise and custom tools. 

Production Readiness Gaps

Production deployment requires high availability, backups, and disaster recovery—considerations often overlooked during PoCs. Some graph databases lack battle-tested replication, clustering, and monitoring solutions. Integrating with enterprise logging and DevOps pipelines requires additional effort that can derail production transitions. In the next blog on this topic, we will present strategies for integrating logging into a PoC and production releases.

Scaling Limitations

Graph databases often struggle with horizontal scaling compared to relational databases. While this isn’t apparent in small PoCs, production deployment across multiple servers can reveal significant challenges. As graphs grow larger and more complex, query performance can degrade dramatically without careful tuning and indexing strategies. We will explore how to thoughtfully scale graph efforts in the next blog on taking projects from PoC to Production.

Security and Compliance Challenges

Access Control Complexity

Graphs connect data in ways that complicate fine-grained access control. In a relational system, you might restrict access to certain tables; in a graph, queries traverse multiple node types and relationships. Implementing security after the fact is tremendously complex. Demonstrating that a graph solution can respect existing entitlements and implement role-based access control is crucial. 

Sensitive Data and Privacy Risks

Graphs can amplify privacy concerns because of their connected nature. An unauthorized user gaining partial access might infer much more from relationship patterns. This interconnectedness raises security stakes—you must protect not just individual data points but relationships as well.

Regulatory Compliance

Regulations like GDPR, HIPAA, or PCI present unique challenges for graphs. For instance, GDPR’s “right to be forgotten” is difficult to implement when deleting a node might leave residual links or inferred knowledge. Auditing requires tracking which relationships were traversed, and demonstrating data lineage becomes complex. If compliance wasn’t planned for in the PoC, retrofitting it can stall production deployment.

Financial and ROI Challenges

Unclear Business Value

Justifying a graph project financially is tricky, especially when benefits are long-term or indirect. A PoC might show an interesting capability, but translating that into clear ROI is difficult if only one use case is demonstrated. Without a strong business case tied to measurable Key Performance Indicators (KPIs), projects struggle to secure production funding.

Scaling Costs

PoCs often leverage free or low-cost resources. However, production deployment requires enterprise licenses, robust infrastructure, and high-availability configurations. An enterprise-level knowledge graph spanning multiple use cases can incur significant long-term costs. These financial requirements can shock organizations that didn’t plan for them.

Operational and Talent Expenses

Beyond technology costs, successfully operating a knowledge graph requires specialized talent—data engineers, knowledge engineers, and graph database administrators. While a PoC might be built by a single person or small team, maintaining a production graph could require several dedicated staff. This represents a significant ongoing expense that organizations often underestimate.

Competing Priorities

Every project competes for finite resources. Graph initiatives promise strategic long-term benefits but may seem less immediately impactful than customer-facing applications. Organizations focused on quarterly results may abandon graph projects if they don’t show quick wins. Breaking the roadmap into phased deliverables demonstrating incremental value can help maintain support.

Data Governance and Scalability Challenges

Ontology and Data Stewardship

Knowledge graphs require consistent definitions across the enterprise. Many organizations lack ontology expertise, leading to inconsistent data modeling. Strong governance is essential to manage how data elements are defined, connected, and updated. Without data stewards responsible for accuracy, production graphs can become unreliable or inconsistent, undermining user trust.

Conclusion

Transitioning a graph database or knowledge graph from PoC to production involves multiple challenges across organizational, technical, security, financial, governance, and talent dimensions. Many promising PoCs fail to cross this “last mile” due to one or more of these issues.

In Part Two, I’ll outline a holistic strategy for successful graph initiatives that can effectively transition to production—incorporating executive alignment, technical best practices, emerging trends like GraphRAG and semantic layers, and the critical people-process factors that make the difference between a stalled pilot and a thriving production deployment.

The post Graph Solutions PoC to Production: Overcoming the Barriers to Success (Part I) appeared first on Enterprise Knowledge.

In this presentation, Hughes discussed an underexplored dimension of GraphRAG–the integration of image–by introducing Multimodal GraphRAG, an innovative framework that brings image data to the forefront of graph-based reasoning and retrieval. He demonstrated how this approach enables more comprehensive understanding of images, amplifying both the depth and accuracy of insights. Attendees gained insight into:

• How mmGraphRAG works;
• The integration of vision models, hypervectors, and graph databases;
• BAML agentic workflows; and
• Real-world applications and benefits for mmGraphRAG.

The post Multimodal Graph RAG (mmGraphRAG): Incorporating Vision in Search and Analytics appeared first on Enterprise Knowledge.