When Microsoft’s 365 Copilot first arrived on enterprise desktops, it was a model service, rarely breaking a single permission. However, it made every single forgotten permission searchable. A Gartner survey found almost 132 IT leaders had delayed Copilot rollouts because of the data oversharing in their organization. None of these organizations had an issue with Copilot; it was their internal data handling practices that would’ve made using Copilot a problem. In short, they had a metadata management problem where no one in the organization could tell what each file contained, who owned it, how sensitive it was, and who should be allowed to see it, all at scale.
Metadata refers to “data about data”. Akin to a control lane, it informs organizational decision-making about its data, such as who can query a table, whether a document can feed an AI model, and which records fall under GDPR. Without these attributes, every downstream system ends up guessing, and like every guess, some decisions are wrong, and in this case, fatal.
The numbers correspond to how critical knowing the data about data has become. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data, and 63% of organizations either don't have or aren't sure they have the right data management practices for AI. The overall security costs are also clear, with IBM’s 2025 Cost of a Data Breach report finding that 97% of organizations that suffered an AI-related security incident lacked proper AI access controls, 63% had no AI governance policies at all, and high levels of shadow AI added an average of USD 670,000 to breach costs.
The following guide explains what metadata management means, its various types, how it works, and most importantly, why security, privacy, and AI governance now depend on metadata management as much as analytics ever did. Read on to learn more.
Not all metadata does the same job. The distinction between the various types is important because the value of metadata management comes from linking these different types together for different purposes.
Technical metadata describes the physical structure and format of the data. This includes schemas, table and column names, data types, field lengths, primary and foreign keys, file formats, storage locations, sizes, partitions, and source-to-target mappings in integration pipelines. This kind of metadata is generated automatically by data warehouses, APIs, and file systems, making it the easiest to harvest at scale.
In practice, data engineers, architects, and platform teams rely on this kind of metadata to build pipelines, plan migrations, and troubleshoot failures. However, it lacks any context. A column in a dataset may be described fully in technical terms and still tell a layman, such as a business analyst, a privacy officer, or even an AI model, nothing about what it holds, who owns it, and whether it should be used.
The meaning is something business metadata provides. This includes glossary definitions, business rules, KPI calculations, ownership and stewardship assignments, data domains, and the relationships between business concepts and the physical fields that store them. Think of it as the contextual bridge that connects something like “cust_em” to "customer email address, primary contact channel, owned by the CRM team, populated at account creation”.
Moreover, unlike technical metadata, business metadata is created by people, leading to its biggest risk. Glossary terms and business rules are never formally documented and exist only as knowledge in the heads of long-tenured analysts. This means two different teams may end up reporting different figures for “active customers” since finance counts anyone billed this quarter and marketing counts anyone who logged in this month, and neither definition is written down where the other teams can find it.
Operational metadata records how data performs in a production environment, such as where it came from and what transformed it (lineage), when it was last refreshed, which jobs produced it and whether they succeeded, how often it's queried, who accesses it, and what quality checks it passed or failed. This form of metadata is mostly generated in pipelines, orchestration tools, warehouses, and BI platforms as a byproduct of running.
This is the metadata that helps answer questions like, “Can this number be trusted?” For example, when a revenue dashboard shows a figure that looks wrong, operational metadata traces it upstream to the job that failed at 3 a.m. or the source table whose schema changed without notice. Moreover, it acts as the raw material for active metadata by providing usage patterns, freshness signals, and lineage changes that can trigger alerts, certifications, or access decisions automatically rather than waiting for a quarterly review.
Governance and sensitivity metadata describes all the rules that apply to data. This covers classification labels such as PII, PHI, or payment card data, sensitivity tiers, the regulations and jurisdictions in scope, retention and deletion requirements, the purpose and legal basis for processing personal data, consent status, and the security controls already applied, such as encryption or masking. This is essentially the metadata on which policies act. A mature program connects the asset’s technical foundations to the governance controls in place, such as what classification it carries, which policies apply, and what regulatory scope it falls under.
The importance of such metadata has risen rapidly in the AI age, as it covers critical policy considerations such as questions like whether a customer table can be used to fine-tune a model, whether an internal copilot may retrieve a contract, and whether a chatbot response could expose regulated data- all questions answered by sensitivity metadata or not answered at all. Most of this cannot be read from a schema; it depends heavily on automated discovery and classification to keep pace with data that's being constantly created, copied, and modified, especially in unstructured stores where traditional catalogs are weakest.
Metadata management is less of a tool and more of a repeatable cycle with 5 distinct stages, as described below:
- Discovery & Collection: Connection to databases, warehouses, lakes, SaaS applications, file shares, and pipelines to harvest technical and operational metadata automatically to find data that may have been missed by others;
- Cataloging: Organizing into a searchable inventory, with each asset having a traceable record;
- Glossary & Stewardship: Assigning owners and business definitions added to ensure records provide the same common context to all other teams outside engineering;
- Classification: Tagging each asset with what it contains and its sensitivity via automated means rather than manual reviews;
- Policy Enforcement: Closing the loop through tags and ownership assignment for better access controls, retention rules, masking, and compliance documentation and reporting.
Each of the stages feeds the other and relies on every step being carried out as effectively as possible. Moreover, this illustrates why organizations that treat metadata management as a one-time static exercise tend to always be playing catch-up as data is being created, copied, accessed, and modified continuously, requiring an oversight mechanism that runs continuously too.
Historically, metadata management has been a passive exercise as metadata was collected on a fixed schedule, stored in a catalog, and only accessed for consultation by people who were looking for it. Hence, the catalog was more of a reference document, with its accuracy depending on how recently someone had run a crawl and how diligently the entries were updated by stewards. However, in this arrangement, the catalog would drift out of sync with the data eventually.
On the other hand, active metadata management is much more direct by collecting data continuously in response to each event, such as a schema change, a new table, a spike in query volume, or even a failed quality check. Additionally, instead of waiting to be read, it triggers pre-approved actions. Consider a column in an Excel sheet newly classified as personal data; it can now trigger masking policy measures for every downstream dashboard it’s a part of.
However, none of these approaches are finished on their own. Active metadata still requires glossary definitions and ownership that only people can provide, while passive metadata can still be of use in instances where data is rarely expected to change or be significantly altered.
The most immediate and important benefits of metadata management for an organization are as follows:
- Faster Discovery: Analysts and engineers find the right dataset in minutes instead of asking around, as ownership, definitions, and freshness are attached to the asset itself.
- Trust In Numbers: Lineage and quality signals show where a figure came from as well as its recency.
- Audit-ready Evidence: Classification, retention, and access records exist for examination as part of internal or regulatory audits.
- Minimal Blindspots: Sensitivity metadata informs security teams which data stores actually matter, ensuring controls and reviews are optimized to target where the exposure is likely to be.
- Controlled AI: Models and agents can be limited to data that's been classified as appropriate.
Metadata management has traditionally been the domain of analytics teams. However, security, privacy, and AI governance are increasingly the most scrutinized departments in an organization. And each of these relies on the same metadata, and most don’t have the right access to it. The most immediate question for each of these departments related to metadata is as follows:
Security – Which Access Actually Matters?
While most identity systems know who has access to which systems, they do not know what’s inside those systems. As a result, every entitlement is deemed the same in an access review.
Sensitivity metadata resolves that, as when classification is made part of the data store, access reviews can take into account which credentials should reach which systems.
Privacy – Where Is This Particular Data & Why Do We Have It?
At their core, privacy obligations are metadata questions. A data subject access or deletion request requires an organization to know every system that holds information about that particular user.
A RoPA requires thorough documentation with information on its purpose, the categories of data involved, who receives it, and how long it's kept. These are all essentially metadata fields.
AI Governance: Is This Data Eligible For This Model’s Training?
Every AI system must begin by considering which data goes in, whether that's a training set, a fine-tuning corpus, or the documents indexed for retrieval. Most organizations continue to make that decision on the basis of folders or databases, meaning the key eligibility question is “does the pipeline have access?” rather than “should it?”
Metadata would make eligibility a policy rather than an accident, where every dataset’s use is preceded by a check against what the model is allowed to use; i.e., support transcripts containing payment card data get masked or excluded before embedding, records collected for one purpose don't quietly become training data for another, and the lineage of what did go in is recorded for the documentation that AI regulations increasingly demand.
Most metadata management programs do not fail because the concept is hard or difficult to implement; they do so because the data evolve, and yet the program doesn’t. The following problems repeatedly come up, with each becoming increasingly more troublesome as AI systems begin consuming enterprise data more directly.
Traditional metadata was designed and developed in an environment full of databases and warehouses, in the presence of schemas that can be harvested. However, contracts, emails, chat threads, presentations, and shared drives have no schema to read, making them the least described data in the enterprise. Moreover, they also function as the data internal copilots and retrieval-based AI tools interact with first, making the Copilot overpermission problem described earlier a metadata problem before it became an AI problem.
A catalog that’s full of scheduled crawls will only be accurate the morning after the crawl and less so the day after. As more tables are added, columns renamed, and sensitive fields copied into new datasets, this problem becomes clearer and clearer. For a human analyst, this staleness is a massive problem, while for AI agents dependent on this catalog, the likelihood of wrong or incomplete outputs is exponentially high.
Business Meaning Is Contextual
Technical metadata harvests itself, while business metadata doesn’t. Things like definitions, rules, and context exist as tribal knowledge held by long-tenured staff, meaning glossaries can remain empty or full of contradictory definitions that remain unreconciled.
Zero Ownership
Metadata management sits somewhere between data engineering, analytics, security, privacy, and compliance. This makes it easier and far more likely for each team to assume the others have it covered. Without a named owner and a chain of accountability for stewards, tooling gets bought, deployed, and quietly abandoned with little or no oversight.
The following practices address the challenges illustrated above.
Automate Discovery & Classification From The Start
Manual registration will prove woefully inefficient and ineffective at keeping pace with how fast the organization creates data, without getting into unstructured stores. On the other hand, automated scans find data across both structured and unstructured systems, classifying it based on what it contains to ensure all coverage remains complete, or as close to completion as possible.
Not every dataset needs to be real-time metadata. However, in the case of warehouses, pipelines, and any store feeding AI systems, this real-time capability is critical. Hence, priority must be given to event-driven collection, so schema changes, new sensitive data fields, and lineage breaks are all flagged immediately rather than after the next crawl.
Classification, regulatory scope, purpose of processing, and legal basis are all factors that governance policies rely on and act on. Recording them and doing so in one repository alongside scheme and lineage is necessary, as opposed to a separate privacy inventory, to prevent misalignment.
A catalog with no owners will mean stale entries, while a glossary with no owners will mean contradictions. Hence, a single owner being named ensures the appropriate level of accountability and transparency can be established right from the start.
Copilots and agents do not have to respect folder structure or institutional memory, as their retrieval process is based on the most efficient way of doing so. Hence, organizations must govern what a model can retrieve by classification and sensitivity metadata, and not just by inherited permissions.
How Securiti Helps
Most of the traditional metadata management tools were built to help people find their data. But Securiti, now a Veeam company, has the DataAI Command Platform, built to help organizations control it. This helps them treat metadata as a layer that controls how it runs rather than as a separate cataloging project on its own.
The DataAI Command Platform contains all the necessary solutions as modules such as Data Discovery & Classification, Data Catalog, Data Lineage, and Data Quality, to cover all the necessary types of metadata without requiring business users to register each asset manually. Most importantly, through its Sensitive Data Catalog, it helps users capture the security controls and privacy context attached to each asset, including encryption and masking status, purpose of processing, and legal basis, alongside the schema and lineage. Finally, the Data Access Intelligence & Governance then ranks access by sensitivity and enforces access controls.
Request a demo today to see how the DataAI Command Platform discovers, classifies, and governs metadata across structured and unstructured data.
Some of the most commonly asked questions about metadata management are as follows:
