Context Management: The Foundation for Trustworthy AI Agents

Imagine you’re a sales engineer. It’s Monday morning, and your CRO storms in with urgent news: Southeast sales have dropped 20%, and the board meeting is in 48 hours. Here’s how the next two days go:

• Discovery chaos: 50 Slack messages later, you’re drowning in outdated wikis and five datasets all named “sales_final_v2″, none with the right region field.
• Access gridlock: IT tickets pile up. Approval delays stretch. Wrong permissions block critical data. Compliance issues surface.
• Quality concerns: When you finally get access, the data has quality issues that require an all-nighter to resolve.
• A ticking clock: 42 hours on discovery and access, six hours on actual analysis, and you’re still no closer to understanding why sales dropped.

This isn’t only an efficiency problem. When people struggle to find and verify the right data, they build workarounds: shared credentials, ungoverned exports, shadow datasets. Every one of those is a governance and security exposure that nobody signed off on.

Now imagine this scenario multiplied by a thousand AI agents working simultaneously at machine speed, each one hitting the same obstacles (fragmentation, access controls, quality issues) but now at scale. Every large language model powering these agents needs relevant information delivered to its context window. Without a system to provide that critical context reliably, agents either stall or hallucinate. And here’s the twist that makes it worse: AI agents love to agree with us, often confirming our biases and telling us we’re exactly right (even when we’re not).

We’re combining all the problems humans encounter working with data, plus the unique challenges of AI. That’s why so many agentic AI initiatives never hit production, and why the ones that do often fail to deliver expected value. In fact, Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027.

Our own research points in a similar direction. For the 2026 State of Context Management Report, we commissioned TrendCandy to survey 250 IT and data leaders. 88% were confident or very confident that they already had a fully operational context platform serving both humans and AI agents. In the same survey, 61% said they frequently delay AI initiatives because they can’t trust their data, and 87% named data readiness as their biggest impediment to putting AI into production, even as 90% called their organization AI-ready.

That gap is the story of enterprise AI right now. Most organizations believe the context problem is solved. Very few have solved it.

The solution: context management

The answer isn’t more sophisticated models or better prompt engineering. We need a new approach, one that addresses the foundational infrastructure AI agents need to work reliably and securely at enterprise scale.

At DataHub, we’ve coined the term “context management” to describe this emerging category. We’re introducing this framework because the industry needs language to talk about what’s missing: the systematic, organization-wide approach to delivering reliable context that goes beyond point solutions and individual applications.

We’re not the first to see that agents need better context. But we are the first to recognize that the solution isn’t better context engineering within each application. It’s context management across the enterprise.

What is context management?

Context management is the organization-wide capability to reliably deliver the most relevant data to AI context windows, enabling the governed and enterprise-scale deployment of agents. In this usage, ‘context’ refers to the combination of structured metadata (schemas, lineage, quality metrics) and unstructured knowledge (documentation, business definitions, institutional expertise) that AI models need to make informed decisions.

It’s context engineering applied systematically across your entire organization, not just within individual applications.

The three Rs: what makes context work

If models could speak (and increasingly, they sort of can), they’d tell us they need three things from context:

1. Relevance

Context must be timely and domain-appropriate. Last year’s data won’t help with today’s crisis. Agents need semantic search capabilities to find what matters for the task at hand, not just what exists.

2. Reliability

Context must be trustworthy with clear provenance. Where did this insight come from? Can I verify its lineage? This becomes critical for agent-to-agent handoffs and audit requirements. When an agent makes a business decision, you need to trace it back to authoritative sources.

3. Retention

Context must persist across conversations and invocations. Agents need to learn from past experiences, retaining conversation history and building institutional knowledge over time. Without retention, every interaction starts from zero, leading to wasted resources, missed patterns, and no ability to track progress on complex, multi-step tasks. Retention is also a maintenance problem, not just a storage one: maintaining critical context means catching definitions that drift, sources that go stale, and knowledge that quietly stops being true.

Traditional context engineering delivers these capabilities within individual applications. Context management delivers them across your entire enterprise.

Why context management matters now (and why it’s urgent)

To understand why context management is critical, let’s trace how we got here and where each approach hits its limits.

Prompt engineering: the single-shot era

Prompt engineering taught us to ask questions precisely, provide good examples, and format responses carefully. It worked for one-off queries but broke down when tasks required large knowledge bases or multiple steps. You simply can’t fit your entire customer database or product catalog into a single prompt, no matter how large your context window gets.

RAG (retrieval-augmented generation): the search solution

RAG (Retrieval-Augmented Generation) was a breakthrough, and remains a powerful pattern for grounding AI model responses in enterprise knowledge. Using embeddings and semantic search, retrieval systems can pull relevant context from large knowledge bases without overwhelming the model. 

But RAG’s reliability fundamentally depends on the quality and governance of the underlying context. Without that foundation, each team builds their own RAG pipeline, choosing their own vector database, embedding model, and retrieval strategy. There’s no consistency, no shared understanding of what’s reliable, and no way to ensure that every RAG-powered agent pulls from a trustworthy, governed knowledge base.

Context engineering: the toolkit without the manual

Context engineering brought together memory, tool calling, structured outputs, guardrails, and sophisticated prompt management into a cohesive toolkit. It gives you fine-grained control over input parameters, meaning what goes into a new context window and how it’s structured. It’s powerful, but it leaves the most critical questions unanswered:

• Where does this context come from?
• How do I know it’s trustworthy?
• How do I ensure consistency across applications?
• How do I govern access at scale?

Context engineering gives you the LEGO blocks to fill a context window. It doesn’t tell you which blocks to use, where to find them, or whether they’re safe to build with.

Context engineering vs. context management: the critical difference

You’re probably wondering, what’s the difference between context engineering and context management?

Context engineering solves the problem within a single application. It’s the techniques and tools one team uses to fill their agent’s context window effectively. It ends up being artisanal, bespoke, and isn’t well set up to scale across an organization.

Context management is what gives it those superpowers by solving this across your entire enterprise. It’s the organizational capability that ensures every agent (regardless of which team built it) can reliably discover, trust, and use context. It’s systematic, governed, and built for scale.

Think of it this way: Context engineering is like each development team writing their own authentication system. Context management is like implementing enterprise SSO. Both get you logged in, but only one is maintainable when you have hundreds of applications.

Context engineering solves the context problem within applications. Context management solves it across your entire enterprise.

Where the context platform fits

Context management is the capability. A context platform is the infrastructure that delivers it.

The distinction matters the moment you start buying. A capability can be described in a strategy document. A platform has to ingest from many systems in your estate, hold definitions that survive expert review, enforce policy at retrieval time, and serve agents at machine speed. One is what you decide to do. The other is what you run.

Critically, a context platform is shared infrastructure, and that’s the distinction that matters most. Every team building agents today is also building some form of context solution: a RAG pipeline, a vector database, a set of prompt templates, a custom retrieval layer. Each one works inside a single application. Each one breaks down at organizational scale, leaving you with inconsistent knowledge bases, duplicated effort, contradictory definitions, and no way to audit what an agent accessed or why it decided what it decided.

A context platform solves this the way enterprise SSO solved authentication, by making context a shared, governed capability every team and every agent can rely on rather than a bespoke build per team. When one team validates a business definition, every agent in the organization inherits it. When a schema changes, every downstream consumer learns about it through one platform instead of through downstream failures.

Why enterprises need context management

We’re in the middle of an AI gold rush. But volume of deployment isn’t the same as value from deployment. In our 2026 State of Context Management Report, two-thirds (66%) of the 250 IT and data leaders surveyed report AI models in their organization producing biased or misleading insights, and they attribute it to data infrastructure that isn’t mature enough to supply sufficient context.

• Fragmentation at scale: Each team is building bespoke context engineering solutions. One team uses Pinecone for embeddings. Another uses Weaviate. A third team built their own vector database. And when agents are deployed across different platforms (Azure OpenAI, Google Vertex AI, open-source models) the fragmentation compounds. Each platform has different context limits, different security models, and no shared source of truth. Your customer-facing agent and your internal agent aren’t using the same knowledge base, so they’re giving different answers to the same questions.  This isn’t hypothetical. In the 2026 State of Context Management Report, 57% of IT and data leaders say they duplicate AI efforts across departments for lack of a comprehensive, unified context graph.
• The microservices lesson we forgot: Remember when Uber ended up with five times as many microservices as engineers? They eventually had to pull back and establish central standards to maintain sanity. We’re watching the same pattern emerge with AI, but we’re moving much faster this time.
• Compliance nightmares ahead: As agents gain autonomy to make decisions and take actions, regulatory scrutiny will intensify. More than half (53%) of the IT and data leaders in our 2026 State of Context Management Report frequently or very frequently hit AI-related compliance issues caused by a lack of data provenance. Without context management aligned with frameworks like the NIST AI Risk Management Framework, you’ll struggle to audit what agents did or why they did it. This creates significant exposure under regulations like GDPR and HIPAA, where you must demonstrate data minimization, enforce retention policies, and provide audit trails on demand.
• The trust crisis: What’s at stake isn’t just inefficiency. It’s trust. When agents make decisions based on unreliable context, when they can’t explain their reasoning, when they duplicate work or contradict each other, organizations will stop delegating meaningful work to them.

The real risk isn’t that AI will fail. It’s that we’ll lose trust before AI gets a chance to succeed.

Building a secure architecture for agentic context access

Enterprise data leaders asking “how do I give my AI agents secure, reliable access to enterprise data?” are asking the right question. But the answer requires rethinking how context flows through your organization.

Traditional approaches treat security as a perimeter problem: Lock down the data, then grant access case by case. But agents operate differently than humans. They make thousands of requests per minute, they chain together multiple data sources in a single workflow, and they can’t exercise judgment about whether an access pattern “feels wrong.”

A context platform provides the infrastructure layer for secure agentic access:

• Centralized retrieval, not scattered access: Rather than giving agents direct access to dozens of data systems, a context platform creates a unified retrieval service. Agents query the context layer, and the context layer helps build the right queries for your data systems. This creates a natural control point for authentication, authorization, and audit logging.
• Document-level authorization at query time: Access controls must be enforced before context reaches the model, never after. A context platform applies fine-grained policies (RBAC, ABAC, or attribute-based rules) at the retrieval layer, ensuring agents only see what they’re authorized to see.
• Provenance and audit trails: Every piece of context delivered to an agent should carry metadata about its source, freshness, and the policies that permitted access. This creates the explainability that compliance teams need and helps mitigate threats identified in the OWASP Top 10 for LLM Applications, including prompt injection and sensitive information disclosure.
• Network isolation where it matters: For sensitive workloads, a context platform can operate within VPCs and private endpoints, ensuring that important context never traverses the public internet.

This isn’t security theater bolted onto an AI project. It’s security architecture built into the foundation, exactly where it belongs.

The DataHub Context Platform

We’ve been building the foundation for context management from day one. In May 2026 we shipped the platform layer on top of it.

DataHub architecture enables context management

As a metadata platform built on an event-sourced architecture, DataHub already delivers the core capabilities AI agents need:

What the context platform does

The DataHub Context Platform sits between your analytics agents and the data systems they query. Four capabilities do the work.

Context Ingestion

Connects your full data estate through 100+ source connectors spanning warehouses, lakes, BI platforms, SaaS applications, streaming systems, and ML platforms, plus unstructured sources like Confluence and Notion. Everything lands in a single context graph that updates continuously through an event-driven pipeline rather than on a batch schedule. When a table schema changes or a quality issue emerges, agents know within seconds. The unstructured half matters as much as the structured half. 

Context Intelligence

Reads the technical exhaust your warehouse already produces. Query history, dbt projects, and BI dashboards contain the join paths, filter predicates, and aggregations that answered similar questions in the past. Context Intelligence extracts them into a semantic index, so an agent retrieves a pattern with a track record instead of reasoning from a blank table. This removes the months of manual semantic modeling that usually gate reasonable agent accuracy.

Context Hub

Gives domain experts a workspace to review, correct, and approve machine-proposed context before agents ever see it. Auto-generated context lands in draft by default. When two departments compute the same metric two different ways, the conflict surfaces and routes to a person to resolve rather than propagating silently into every agent downstream.

Context Activation

Delivers validated context to Snowflake Intelligence, Databricks Genie, Claude, Cursor, and custom LangChain agents through MCP, the Agent Context Kit, prebuilt skills, APIs, and SDKs. The DataHub MCP server works as a centralized retrieval service, exposing the context graph through a standard interface that can sit behind your enterprise API gateway. Teams are already running it in production: data engineers at Block are investigating issues in seconds, tracing impact across their entire stack. And because agents receive pre-validated context rather than raw schema, they spend fewer tokens per question.

The context graph underneath it

The context graph connects datasets, columns, dashboards, ML models, business glossaries, metrics, people, and systems through meaningful relationships. It captures lineage, ownership, documentation, and quality metrics, and it holds the semantic definitions and unstructured knowledge that give all of that meaning. Comprehensive audit logs capture every context access for compliance and security investigations.

Why the architecture matters

Most products entering the context category were built around a relational store fronting a browsable web UI, and are now being retrofitted to serve agents that query metadata at rates the original designs never anticipated. In its impact brief on the release, EMA notes that DataHub was built on a metadata graph and event log from the start, treating the catalog interface as one consumer of the graph rather than the product itself. Lineage, data quality, and access policy sit in the same layer as the semantic intelligence, so what reaches the agent is governed context, not meaning alone.

Every change to the graph carries a timestamp and a version. Because the substrate is an event log, the platform can reconstruct how a definition or a lineage edge looked on any given date, which turns context from a current-state lookup into an evidence trail.

None of the underlying work is new ground. Discovery, lineage, reliability, and governance have been shipping for years, and they are the reason the context platform has something to stand on. Column-level lineage traces every piece of context back to its source. Quality monitoring, freshness tracking, and usage analytics keep it accurate. Automated governance workflows decide who and what gets access, and when. The context layer doesn’t replace any of it. It puts it in front of agents.

What this looks like in production

Pinterest built its context layer on DataHub. Its analytics agent became the most-used internal agent at the company, running at 10x the usage of the next most-used agent within the first two months of deployment, alongside a 60x improvement in the speed of answering analytical questions. Questions that once required hours of exploration now return trusted answers in minutes, with every analyst’s institutional knowledge made accessible to everyone.

Miro started with Snowflake metadata alone, and its analytics agent answered roughly half of its benchmark questions correctly. After adding DataHub context derived from query history, cross-source signals, and business definitions, accuracy reached roughly 90%, close to a 2x improvement.

FIS grounds Databricks Genie in enterprise context through DataHub.

“We got the full business and technical context, not just the column names. We have domains, ontologies. It’s all in there. It’s shareable.”

Frank ShowalterVP of Data Engineering & Governance, FIS

Better Genie accuracy wasn’t the end of it. As Showalter puts it: “The net-net of it was we got better Genie accuracy. We got a net new line of business, a new product delivered.”

Our vision: transforming enterprise data into context

We’re defining context management and building the platform to deliver it.

Having spent 10 years building metadata infrastructure at LinkedIn and now watching AI initiatives at the world’s most advanced technology companies, we’ve been the first to see where this is heading, and the chaos that’s coming if enterprises don’t get ahead of it. 

While others are focused on building better agents, we’re focused on building the infrastructure that provides agents with the trusted context that they need.

We’re committed to evolving DataHub to set the standard for the context management category. Our roadmap is driven by the problems we’ve seen firsthand as organizations deploy agents in production: fragmented context sources, inconsistent reliability, governance gaps, and the inability to scale beyond pilot projects.

This isn’t about adding vanity AI features. It’s about fundamentally reimagining how enterprises deliver context to AI systems with the same rigor, governance, and scalability they’ve applied to data management over the past decade.

As the category matures, so will our platform. We’re building alongside our open source community of 16,000+ practitioners who are on the front lines of agent deployment, learning what works and what breaks in production environments.

Here’s what becomes possible once context is shared infrastructure rather than a per-team build:

• A Data Custodian Agent that continuously monitors your data ecosystem, identifies unused assets driving up storage costs, orchestrates approvals from the right stakeholders, and cleans up resources automatically—all while maintaining full audit trails.
• An AI SDR that doesn’t hard-code connections to Salesforce but intelligently discovers your authoritative data source (whether that’s Snowflake, HubSpot, or a hybrid during migration) and uses verified context to engage prospects without damaging your brand.
• An Experience Optimizer that analyzes customer feedback from Zendesk, product telemetry from Mixpanel, and inventory data from your warehouse to identify root causes of user frustration. Then, automatically proposes, tests, and validates product improvements.

None of these agents are guessing where to find context or which source to trust. They’re reading from a context platform that already understands the enterprise. That’s the difference between an agent that demos well and an agent you’d let near a production decision.

How to get started with context management

Here’s what you can do today to start building context management capabilities that scale across your organization.

Step 1: Map your context landscape

Before you can manage context, you need to understand where it lives. Conduct an inventory across three dimensions:

• Technical context: Catalog data lineage, schema definitions, quality metrics, version control, and technical dependencies. This helps agents understand how to interpret and process your data.
• Operational context: Map runtime metrics, access patterns, data SLAs, system dependencies, and operational policies. This tells agents which sources to trust and their performance characteristics.
• Business context: Capture the human knowledge and governance frameworks that live in your business glossary, docs, wikis, Slack channels, access control policies, or domain expertise held by specific teams. This tells agents about data’s organizational meaning. 

The output: a clear picture of where authoritative context exists and where the gaps are.

Step 2: Identify and prioritize agentic use cases

Not all agentic applications are created equal. Start with use cases that have:

• High-impact business value: What would move the needle if automated? Customer support triage? Data quality remediation? Anomaly investigation?
• Manageable scope: Choose bounded problems with clear success criteria. A data custodian that archives unused tables is more achievable than an agent that rewrites your entire data architecture.
• Available context: Pick use cases where you already have (or can quickly build) the context infrastructure. Don’t start with applications that require context from systems you haven’t cataloged yet.
• Risk tolerance alignment: Balance innovation with governance. Start with internal-facing agents where mistakes are reversible before deploying customer-facing applications.

Create a prioritized roadmap with 2-3 pilot use cases that can demonstrate value quickly.

Step 3: Build your knowledge graph foundation

Here’s where context management becomes real. You need a unified knowledge graph for your enterprise data that connects your technical, operational, and business context into a single queryable system.

This is what DataHub was built for. The DataHub Context Platform ingests metadata and unstructured knowledge from across your stack, captures the relationships between datasets and owners, makes context searchable through natural language, and tracks lineage and quality metrics in one place.

Step 4: Deploy and measure pilot agents

Launch your prioritized use cases with tight feedback loops:

• Instrument everything: Track which context agents access, how often, and with what outcomes. Measure accuracy, latency (including P95/P99 for SLA compliance), error rates, and business impact. Monitor cost per request to prevent runaway expenses as you scale.
• Start in observation mode: Let agents recommend actions before they take them. Build trust with stakeholders before granting autonomy.
• Create feedback mechanisms: Make it easy for users to flag when agents get things wrong. Use these signals to improve.
• Iterate on context delivery: As you learn what agents actually need, refine your knowledge graph to meet agents where they are.

Step 5: Scale across the organization

Once pilots prove value, expand systematically:

• Standardize patterns: Document what works. Create templates for new agentic applications that inherit governance, context access patterns, and predefined SLAs for latency and availability.
• Build organizational muscle: Train teams on context management principles. Make it part of your AI development culture, not an afterthought.

Treat context as shared infrastructure and your agents inherit it. Treat it as a per-project concern and every team pays the same cost again.

Context management isn’t a one-time project. It’s a capability you build incrementally. Start with inventory, prove value with pilots, then scale systematically.

One question separates a context platform from a catalog with AI features bolted on: what happens when you ask about something that changed this morning? If the answer is that you’ll see it tomorrow, the context reaching your agents is already stale.

Ready to build trustworthy AI agents?

Build context management capabilities now and your agents can be trusted with meaningful work. Skip it and you get a thousand eager agents giving confident wrong answers.

See DataHub in action: Take our interactive product tour to explore how DataHub Cloud powers context management at scale.

Dive deeper into context management: This post scratches the surface of what I covered in my CONTEXT keynote. Watch the keynote on demand to get the full story.

The future where PMs, engineers, and data practitioners can trust AI to ship great products on their behalf requires a foundation of reliable, discoverable, trustworthy context. That future starts with context management.

Originally published November 7, 2025, updated August 19, 2026.

FAQs

What is context management?

Context management is the organization-wide capability to reliably deliver the most relevant data to AI context windows, enabling the governed and enterprise-scale deployment of agents. It encompasses how enterprises integrate, curate, activate, govern, and maintain the quality of context across all AI applications.

What is context management in AI?

In AI, context management refers to the systematic approach of ensuring AI agents and language models have access to relevant, reliable, and trusted information when making decisions. Rather than each AI application managing its own context independently, a context platform provides the shared infrastructure that ensures consistency, governance, and quality across all agentic AI initiatives.

What is the difference between context engineering and context management?

Context engineering is the hands-on practice of preparing and delivering context to individual AI applications, including selecting what goes into a context window, structuring prompts, and managing retrieval.

Context management scales these practices organization-wide. Think of context engineering as each team writing their own authentication system. Context management is implementing enterprise SSO. Both get the job done, but only one scales.

Is a context platform the same as a data catalog?

No, though a context platform does everything a data catalog does. Catalogs were built for humans browsing a web interface, with discovery, lineage, governance, and observability as the deliverables. A context platform adds the semantic definitions, documentation, and institutional knowledge that give that metadata meaning, then serves it to AI agents at machine speed with provenance and policy attached. The architectural difference is that catalogs treat the browsable interface as the product. A context platform treats it as one consumer of the graph among many.

How mature is context management in most organizations today?

Less mature than organizations believe. In the 2026 State of Context Management Report, a survey of 250 IT and data leaders, 88% were confident or very confident they had a fully operational context platform. At the same time, 61% frequently delay AI initiatives for lack of trusted data, 57% duplicate AI efforts across departments for lack of a unified context graph, and 53% frequently hit AI-related compliance issues caused by missing data provenance. The gap between self-assessed maturity and operational reality is the defining feature of the category right now.

How much does a context platform improve AI agent accuracy?

It depends on the starting point and the quality of the context you feed it, but the production numbers are substantial. Miro’s analytics agent answered roughly half of its benchmark questions correctly using Snowflake metadata alone. After adding DataHub context derived from query history, cross-source signals, and business definitions, accuracy reached roughly 90%, close to a 2x improvement.

What are the three Rs of context management?

The three Rs are DataHub’s framework for what makes context work:

1. Relevance: Context must be timely and domain-appropriate for the task at hand
2. Reliability: Context must be trustworthy with clear provenance and verifiable lineage
3. Retention: Context must persist across conversations, enabling agents to build institutional knowledge over time

Why is context management important for AI agents?

AI agents inherit all the problems humans face when working with enterprise data (discovery chaos, access gridlock, quality concerns) but at machine speed and scale. Poor context management leaves agents operating on fragmented, ungoverned, and potentially stale information, which leads to unreliable outputs, compliance risks, and eroded trust. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027.

How do you implement context management?

Implementation follows five key steps: 

1. Map your context landscape across technical, operational, and business dimensions
2. Identify and prioritize agentic use cases based on business value and available context
3. Build your knowledge graph foundation to connect context sources
4. Deploy pilot agents with tight feedback loops and comprehensive instrumentation
5. Scale systematically by standardizing patterns and building organizational capability.

What is a context graph in context management?

A context graph (or knowledge graph) integrates structured metadata and unstructured data into a unified semantic network. It connects datasets, documentation, business glossaries, ownership information, and quality metrics through meaningful relationships—creating the substrate that AI agents need to discover, understand, and trust enterprise context. Unlike simple document stores, a context graph captures how information relates, enabling agents to traverse connections and understand context in depth.

What is the difference between RAG and context management?

RAG (Retrieval-Augmented Generation) is a pattern for grounding AI responses in external knowledge by retrieving relevant documents at query time. A context platform is the infrastructure that makes RAG reliable at enterprise scale. RAG tells you how to pull context into a model. Context management is the discipline that ensures the context itself is discoverable, trustworthy, governed, and consistent across every RAG implementation you run.

What is a secure architecture for agentic context access?

A secure architecture for agentic context centers on four principles: 

1. Centralized retrieval (agents query a context layer, not data systems directly)
2. Document-level authorization enforced at query time
3. Comprehensive audit trails with provenance metadata
4. Network isolation via VPCs and private endpoints where required

This approach aligns with frameworks like the NIST AI Risk Management Framework and addresses threats identified in the OWASP Top 10 for LLM Applications.

How does the NIST AI Risk Management Framework relate to context management?

The NIST AI RMF provides guidance for managing AI risks across the lifecycle. Context management operationalizes key NIST principles by ensuring AI agents work with trustworthy, well-governed data, maintaining audit trails for explainability and accountability, and establishing consistent policies for how context is accessed and used across the enterprise.

How does context management help mitigate threats in the OWASP Top 10 for LLM Applications?

The OWASP Top 10 for LLM Applications identifies critical security risks including prompt injection, sensitive information disclosure, and insecure output handling. A context platform mitigates these threats by enforcing access controls at the retrieval layer (before context reaches the model), maintaining provenance metadata for all context, filtering sensitive information based on governance policies, and providing audit trails that enable detection of anomalous access patterns.

How can I give my AI agents secure, reliable access to enterprise data and context?

Start by establishing a context management foundation: centralize metadata in a knowledge graph, implement fine-grained access controls at the retrieval layer, and create comprehensive audit logging. Use a platform like DataHub to index your data systems, enforce governance policies, and expose context through standardized interfaces like the Model Context Protocol. This gives agents a secure, governed pathway to enterprise context without direct access to underlying data systems.