Scaling DSR fulfillment: Why infrastructure beats workflows

The scaling process

Many organizations have left fully manual DSR processes behind, but even so, workflows and procedures with multiple humans in the loop—often working across legal, compliance, HR and engineering—only work until they don't. The breaking point, when it comes, won’t be gradual, and it could bring massive costs. Organizations have reported spending over two weeks and $1,400 per request when handling DSRs manually, but such figures only tell part of the story. A bigger crisis emerges when DSR volume meets the growing complexity of enterprise data.

Firstly, there’s the time required to fulfill obligations for each DSR, which can require between 10-15 hours of key staff input. When the obligations mushroom in response to growing requests, the numbers stack up quickly. An organization that gets 100 DSRs on a monthly basis might face a resource allocation requirement of over 16,000 person hours annually. That’s the equivalent to 8-9 full-time team members.

Meanwhile, DSR volumes are rising rapidly. Data from EY in 2023, surveying the financial services industry, reported that 60% of organizations had seen a rise in DSRs in the previous 12 months and one in three was now a “bulk” request, creating vast pressure-points on teams and processes. Complaints are also on the rise. EY found that more than half of respondents had received complaints from data subjects, while in the UK, the Information Commissioner's Office fielded over 15,000 complaints about DSR responses in 2023 alone—a 13.5% year-over-year increase.

That 100-DSRs-a-month figure quoted above is nothing outlandish, either: the EY 2023 report quoted one Data Protection Officer of a global financial services provider who said their organization was processing 1,000 DSRs a month on average, with a dedicated team of 20 in place to fulfill them.

The scale is obvious, and it would be bad enough if volume was the only villain. It’s not.

Enterprise data fragmentation creates exponential complexity. User information gets distributed across dozens of internal databases, cloud services and third-party vendors, each maintaining their own unique access patterns, data structures, and even authentication requirements to access them. The coordination task across these disconnected systems creates problems that workflow optimization, or better DSR fulfillment scripts, cannot hope to solve without substantial technical maintenance costs that rise with each new system either added to the mix, or taken away.

A privacy team relying on spreadsheets, email chains and case queues to orchestrate DSR fulfillment, at scale, amid massive data complexity, is almost like trying to conduct the Berlin Philharmonic orchestra by SMS. It just won’t work.

How hidden costs accumulate

Organizations focused on immediate DSR completion costs often miss the larger financial and strategic risks accumulating in the background. Manual or human-in-the-loop fulfillment creates several hidden expense categories that compound over time.

These include:

1. Resource opportunity costs that extend far beyond the direct labor hours spent on each request. When senior engineers and privacy, legal and HR professionals spend hours hunting through databases and coordinating with multiple teams, they're not building strategic privacy programs or supporting innovation initiatives. The obvious cost is the hours they work on it. The unseen one is what they can't accomplish as a result.
2. Regulatory risk exposure grows with volume and complexity. Each manual handoff introduces potential for human error, missed deadlines or disclosures that are incomplete or inaccurate. With data protection authorities increasingly focused on enforcement rather than education, operational failures can quickly result in regulatory penalties and accompanying reputational damage.
3. Competitive disadvantage emerges when organizations become paralyzed by their own compliance processes. While companies with automated infrastructure can confidently expand data-driven initiatives (initiatives which only get more business-critical given the AI imperative), those stuck in workflows face decision paralysis. Each new system or data source increases DSR complexity, and it’s not too many leaps from there to a perverse incentive to avoid innovation because of the downstream headaches it brings.

Why a trusted data layer changes everything

1. As shown above, at their core DSRs are not workflow problems. They are an infrastructure challenge that happens to involve workflows. The distinction matters because it determines whether an automation can actually scale, or whether it simply turns manual processes into digital workflows, with all the old inefficiencies baked in.

Workflow solutions focus on things like request tracking and administrative coordination. They might be good at organizing human effort, but they cannot answer the core question: how can you systematically locate, process and package personal data across multiple disparate enterprise systems? Dashboard solutions and ticketing systems are band-aids on a bullet wound. Infrastructure-first automation, on the other hand, embeds DSR capabilities directly into the data layer, treating request fulfillment as a programmatic operation rather than an administrative process.

This approach enables several capabilities that workflow tools cannot deliver, including:

1. Identity resolution across systems. Rather than running scripts to trawl through databases (or worse, manually doing so), automated infrastructure can trace and monitor data relationships across the entire enterprise data landscape, discovering connections that human processors might miss.
2. Policy-driven execution that applies consistent logic across all data sources. Instead of relying on individual human judgment about what data to include or exclude, infrastructure automation enacts privacy policies in code and applies them systematically, increasing the defensibility of request fulfillments.
3. Audit trails generated at the system level rather than through human documentation. Every action, decision and data transformation gets logged automatically, creating comprehensive evidence for regulatory scrutiny without the additional administrative burden typically associated with that requirement.

This infrastructure approach to DSR fulfillment treats DSRs like other technical enterprise operations: reliable, repeatable, testable, and trusted. Built into the data layer, fulfillment processes can strengthen rather than weaken as enterprise data complexity grows, which it certainly will.

The business math of DSR automation

The switch from scripted or manual (in whole or in part) processes to automated DSR fulfillment creates compelling ROI benefits beyond the obvious cost reduction. Organizations implementing infrastructure-level automation report processing times dropping from days or weeks to hours or minutes—with Ethyca customers achieving DSR fulfillment for straightforward requests in 17.2 seconds on average.

These time reductions can translate into massive cost advantages. Where more laborious processes might cost more than $1,400 per request to fulfill, automated infrastructure can reduce per-request costs to $40 or less. In the example mentioned above, the enterprise fielding 100 DSRs a month and taking 10-15 hours to fulfill each one, that might mean potential savings from costs that could equate to eight enterprise team members to under $50,000 a year.

Other benefits core to the ROI calculation must include that automated infrastructure enables significant additional strategic capabilities, including:

1. Regulatory preparedness for emerging privacy laws, which becomes systemized and automatic rather than reactive and intimidating. Infrastructure that can systematically handle DSRs under existing privacy regulations can quickly adapt to new legislative requirements without any need for a fundamental rebuild.
2. Operational resilience under pressure from regulators, customers, or scale. When DSR volume spikes or regulatory scrutiny intensifies, script-based and even partially manual processes can cause a tsunami of repercussions across an entire organization, whereas automated systems maintain the consistent performance that all great enterprises expect.
3. Confidence in data innovation, which emerges when organizations know they can systematically handle privacy obligations across any new system or data source. Rather than avoiding data-driven initiatives due to compliance complexity, automated infrastructure enables rapid expansion of AI and analytics capabilities.

The upfront investment required to build an automated infrastructure is typically recovered within months while delivering years of strategic flexibility and the operational and competitive advantages that flexibility brings.

How do you know when you're ready?

Savvy organizations everywhere are already recognizing when their existing workflows have reached their scaling limits. The indicators that signal readiness for infrastructure-level automation include:

• Volume and complexity thresholds. Organizations that handle 50+ DSRs annually or see a rapid increase in requests; that manage data across 10 or more internal systems or external vendors; or that have experienced missed deadlines and inconsistent disclosures have all likely outgrown manual fulfillment capabilities.
• Resource allocation patterns. Teams that report spending more time coordinating DSR fulfillment than building strategic privacy programs indicate that operational efficiency gains could unlock significant strategic capacity and all the benefits that would bring.
• Risk tolerance gaps. Organizations that cannot systematically demonstrate comprehensive data discovery and consistent policy application face mounting regulatory exposure. If your legal and privacy teams have come to lack confidence in the completeness and defensibility of existing processes, the time has come.
• Innovation friction. It happens that the growing complexity of DSR fulfillment discourages data-driven initiatives, because teams avoid new systems or data sources due to privacy compliance concerns. If this sounds familiar, then the point where manual processes actively inhibit business growth has arrived.

The first step for many organizations is to start with high-volume systems like CRM platforms and data warehouses, then expand coverage across their data infrastructure. DSR automation can serve as an excellent entry point for broader modernization of the full privacy technology suite, providing immediate value and demonstrating clear ROI for executive stakeholders.

The investment doesn’t require a perfect data inventory, but it will require alignment between privacy, legal and engineering functions. The good news is that all functions stand to benefit, freeing up time and headspace for more strategic, rewarding or profitable initiatives.

Scaling DSR automation and the AI question

The most forward-thinking enterprises will recognize that scaling DSR automation can become a key part of the foundational infrastructure for broader data opportunities (and responding to the data governance challenges that come with them). As AI opportunities unfold in multiple directions at warpspeed, and the attendant AI regulations mature and their enforcement intensifies, this has never been more true.

The same type of systematic personal data discovery and processing needed for DSRs becomes critical for AI model training compliance, algorithmic auditing and data minimization requirements, all outlined in the EU AI Act and other AI legislation being rolled out globally. Taking an infrastructure-level approach to DSRs can serve as a building block for the comprehensive privacy engineering programs that can power AI governance at scale.

Shifting from workflow-centric to an infrastructure-first DSR approach ticks the essential boxes for the growing complexity of DSR fulfillment, of course, but it also has far-reaching benefits beyond that. Enterprises that pursue data-driven growth in an increasingly regulated (and increasingly enforced) environment will need infrastructure that can systematically handle privacy obligations across distributed systems, evolving requirements and increasing complexity and scale.

The big leap is to recognize DSRs, among other privacy obligations, as infrastructure operations rather than administrative tasks. The most successful enterprises will make that leap on the way to building the technical foundation for trustworthy data operations at scale.

Should you treat data governance as an administrative function that comes with growing headaches? Or could you build it into the data layer as programmable infrastructure? Organizations that choose the latter will position themselves for sustainable competitive advantage in an AI-driven, regulation-intensive future.

Ready to move beyond manual workflows? If your team is spending hours per DSR (or if your systems are too fragmented to respond with confidence) it’s time to rethink your foundation. Ethyca embeds DSR compliance into the data layer, enabling scalable, policy-driven automation that works across your entire stack. Book a walkthrough here.

About Ethyca: Ethyca is the trusted data layer for enterprise AI, providing unified privacy, governance, and AI oversight infrastructure that enables organizations to confidently scale AI initiatives while maintaining compliance across evolving regulatory landscapes.