Access requests have become a standing operational load for privacy teams. It’s no longer a question of if you’ll get a “Get a copy” or “Access” request - it’s how many, how often, and how quickly you’re expected to respond.

If your team is handling hundreds or thousands of DSRs across multiple systems, you already know the real challenge isn’t receiving the data - it’s preparing it for safe disclosure.

For global enterprises, DSR volumes are increasing year over year. Regulatory scrutiny is tightening, response windows are narrowing, and enforcement actions are increasingly examining not just whether a response was sent - but whether it was accurate, complete, and appropriately redacted.

But the hard part isn’t collection - it’s disclosure. Data comes back as machine-formatted JSON: nested structures, inconsistent fields, internal metadata, and sensitive attributes mixed into the same payload. 

What you retrieve from systems was never designed for external delivery. Structured records often contain internal system identifiers, payment references, IP addresses, free-text notes, third-party references, and audit metadata - all embedded within complex schemas that were never designed for direct external disclosure.

Preparing it to share safely and clearly takes time, consistency, and care - where one miss can create compliance and trust risk.

And when that preparation depends on manual review, spreadsheets, or ad hoc cleanup processes, the risk multiplies.

Introducing the Mira AI eDiscovery Agent

The Mira AI eDiscovery Agent is a new autonomous AI agent that automates the redaction, transformation, and preparation of structured data returned during Data Subject Requests (DSRs).

It addresses one of the most operationally intensive steps in privacy rights fulfillment - converting raw technical records into accurate, privacy-safe reports ready for disclosure - without requiring manual intervention.

It processes JSON data from automatic integrations and applies your instructions so the data is formatted, filtered, and protected before it’s delivered to the data subject.

Define rules once - the agent applies them consistently at report compilation, turning raw outputs into disclosure-ready results.

Most DSR automation platforms stop at data collection. The Mira AI eDiscovery Agent goes further - automating disclosure readiness itself.

Instead of exporting raw machine data and manually cleaning it, you embed disclosure governance directly into your fulfillment workflow.

The result: disclosure you can trust - consistent, privacy-safe, human-readable outputs at scale, without bottlenecks or last-minute risk.

Closing the Gap Between Data Collection and Defensible Disclosure

Regulatory pressure around data subject access rights continues to intensify. Under GDPR, CCPA, and an expanding array of global privacy frameworks, organizations are required not only to respond to access requests but to do so accurately, consistently, and within defined timeframes.

For enterprises managing high DSR volumes across complex data ecosystems, the operational burden is significant - and growing.

Most DSR automation platforms have focused on data discovery, collection, and workflow orchestration. Yet the step that follows - reviewing, redacting, and preparing structured records for safe disclosure - has remained largely manual.

This creates a critical gap between retrieval and defensible disclosure.

For organizations processing large volumes of structured data returned from multiple integrations, this manual layer introduces:

• Bottlenecks that slow fulfillment timelines
• Inconsistency between similar requests handled by different reviewers
• Over-disclosure risk when sensitive internal attributes are not properly filtered
• Audit exposure when disclosure logic is not standardized or documented
  
  

In regulatory investigations, it’s not enough to prove you delivered data. You must prove that you reviewed it appropriately, applied consistent redaction standards, and controlled disclosure risk. Defensibility requires repeatability - not reviewer discretion.

The Mira AI eDiscovery Agent is designed to automate this transformation layer entirely. Using customer-defined instruction sets, the agent automatically redacts sensitive fields such as payment details, IP addresses, and internal identifiers; strips irrelevant internal metadata; and converts complex JSON records into clean, human-readable reports - all before data is delivered to the requesting individual.

The outcome is not just faster fulfillment - it is standardized, defensible disclosure built into your operating model.

From Manual Redaction to Autonomous Disclosure

Access request fulfillment has traditionally relied on post-collection cleanup.

Before automation at the transformation layer, the process often looked like this:

1. Export raw structured data
2. Manually review fields in spreadsheets or JSON viewers
3. Redact sensitive attributes line by line
4. Reformat outputs into readable reports
5. Conduct additional oversight review

This approach does not scale with growing request volumes. It consumes hours of manual effort per case and introduces variability across responses.

The eDiscovery Agent changes that. Redaction and formatting become instruction-driven and automated, reducing over-disclosure risk while improving clarity for data subjects - and freeing teams to focus on oversight, not repetitive processing.

Disclosure logic moves from individual reviewer judgment to standardized execution. Governance shifts from reactive review to proactive system design.

Built Into the MineOS Operating Model

The eDiscovery Agent is not a separate tool. It is embedded directly into MineOS workflows, running as part of the report compilation process and Autopilot fulfillment - so governance is applied consistently without adding operational overhead.
The eDiscovery Agent operates as an execution layer within MineOS. You define disclosure instructions once - specifying what fields to redact, what attributes to exclude, how to transform nested records, and how to present structured outputs.

At report compilation, the agent autonomously applies those instructions across varying data schemas and integration outputs, ensuring consistent outcomes regardless of source complexity.

Unlike static scripts or one-off manual cleanup, the agent executes governed logic dynamically inside the fulfillment workflow - ensuring that every disclosure reflects the same defined privacy standards.

This integration ensures that disclosure governance operates as a native layer of fulfillment - not as an external review step bolted on after data collection.

Privacy intent becomes executable. Disclosure becomes standardized. Operations become scalable.

Autonomous Privacy Operations, Powered by Mine

Mine’s vision is to operationalize privacy through intelligent infrastructure.

Privacy programs can no longer rely solely on policy frameworks and reactive processes. As data ecosystems grow more complex and regulatory expectations continue to rise, governance must be embedded directly into execution layers.

Agents like eDiscovery embed governance into execution - so teams can scale privacy operations with confidence as volume and expectations rise.

The Mira AI eDiscovery Agent represents a foundational shift toward AI-native privacy operations - where compliance is engineered into workflows rather than manually enforced after the fact.

Governance should not live only in documentation. It should live in systems. With the Mira AI eDiscovery Agent, defensible disclosure becomes an automated capability - not a manual burden.

Now available inside Mira AI agents in MineOS.

Interested in seeing how autonomous privacy assessments work in practice?

Contact us to learn more or request a walkthrough of MineOS.