eDiscovery, legal research and legal memo creation - ready to be sent to your counterparty? Get it done in a heartbeat with AI. (Get started for free)
The GDPR's right of data access, commonly known as the right to make a Data Subject Access Request (DSAR), has put many organizations in a bind. While enabling individuals to understand how their data is processed can support transparency and trust, responding properly to DSARs requires time, resources, and advanced capabilities.
According to surveys, up to 60% of DSARs go unanswered due to businesses struggling to find relevant data locked away in emails, shared drives, and other unstructured sources. Even when organizations can locate personal data, few have efficient processes to handle review, redaction, and compliant delivery at scale. This forces staff to tackle DSARs manually, sifting through information piecemeal.
With GDPR penalties reaching up to 4% of global revenue for non-compliance, organizations feel trapped between risking sanctions and diverting excessive resources. As one CPO remarked, "We aimed to answer DSARs in a month, but our backlog stretches into next year." Others pay consultants hourly, but still require internal effort. The result is overworked staff, overlooked requests, and lingering legal exposure.
Some organizations hoped technology would help, but found generic search tools returned excessive, irrelevant data. Reviewing this for compliance wasted more resources. Though AI offerings seemed promising, many worried customized systems would become obsolete as regulations evolved.
Intelligent search algorithms locate any personal data related to the individual, digging into unstructured sources traditional tools cannot handle. This prevents companies from overlooking information locked in old emails, hidden folders, or formats like scanned documents and images.
Unlike basic keyword search, AI understands context to pinpoint relevant content mentioning the data subject. This significantly cuts down on the volume of results for reviewers. One client reported their review set was reduced 90% compared to exporting all search hits.
Advanced redaction capabilities handle sensitive information, ensuring only appropriate data is shared. Built-in templates redact common personal identifiers like bank details, while smart redaction can automatically protect custom fields like employee IDs. This removes the need to painstakingly black out information by hand.
Compliance is guaranteed through features like verifying request authenticity, logging all access and actions, producing detailed audit reports, and guaranteeing full deletion after fulfillment. This covers organizations in case of disputes while freeing them from constant monitoring.
The system outputs DSAR responses in the required format, whether email, portal, or mailed hard copy. This saves hours of document preparation and delivery compared to makeshift solutions involving spreadsheets or file sharing sites.
Crucially, the AI learns and adapts to new privacy regulations as they emerge across regions. This prevents expensive upgrades and ensures continuous compliance as laws evolve. The system acts as a one-stop shop for global organizations fielding GDPR, CCPA, and other major privacy framework requests.
Early adopters have seen dramatic time and cost savings. DLA Piper reduced time spent on DSARs 92% by switching from manual processing to AI, completing requests in hours instead of weeks. EY cut DSAR costs by up to 70% using intelligent automation.
For industries like banking and insurance handling high DSAR volumes, AI presents massive potential resource and liability savings. One insurer reported slashing DSAR processing expenses by Â£2.3 million annually. Even organizations with fewer requests can benefit from eliminating redundant and manual efforts.
Properly understanding the specifics of each DSAR is crucial for crafting a fully compliant response. However, decoding often vague or broad requests has posed a major pain point. Organizations report spending excessive time deciphering exactly what data an individual is seeking access to across disjointed systems holding emails, documents, profile data, transaction logs, and more.
Without precise clarity on the request scope, organizations often either overlook important data sources, or overcompensate by exporting and reviewing excessive irrelevant information. Both approaches lead to non-compliant responses. Unfortunately, manually dissecting requests is hugely time consuming given factors like ambiguous phrasing and lack of visibility into data storage.
For instance, a request for "all data retained" could intend anything from emails to CCTV footage depending on interpretation. Yet phone or written exchanges to clarify specifics add delays when prompt action is legally required. This forces staff to make assumptions potentially overlooking key sources.
AI alleviates this via sophisticated natural language processing able to deeply comprehend nuanced requests. The system parses convoluted or technical phrasing to determine the true underlying intent. This allows it to identify which data types and sources the request targets based on context cues.
For example, AI can distinguish a request for "transaction records" as pertaining specifically to purchase history logs rather than wider financial data. It also clarifies broad requests like "all personal data" by mapping them to actual data storage like profiles, activity logs, and communications.
The system consults its knowledge base on where different data types reside within the organization's various business units and repositories. This allows it to pinpoint which sources are implicated rather than leaving gaps in understanding.
With precise clarity on which data the request entails, the AI can then execute a tailored search strategy across only relevant sources. This prevents dumping excessive irrelevant data into review queues, or overlooking obscure silos holding personal information.
Once the AI has a precise understanding of the request scope, it can execute a targeted search to pinpoint only the personal data relevant to the request. This prevents organizations from either missing crucial information, or wasting resources reviewing excessive irrelevant data that basic search tools return.
Intelligent search algorithms are able to dig into unstructured data stores that contain the majority of an organization's information, but are opaque to traditional keyword search. This includes messy sources like freestyle email inboxes, shared drives, collaboration platforms, and legacy databases. Manual searches inevitably overlook critical data locked in these silos.
By leveraging advanced natural language processing and optical character recognition capabilities, the AI can extract insights from unstructured text, scanned documents, images, audio, and video. This enables it to surface personal data related to the request even if buried in obscure formats across the organization's sprawling data estate.
The AI goes beyond simplistic keyword matching to understand document context and determine relevance based on meaning. For example, it can identify data subjects mentioned by name, alias, or title rather than explicit keywords. This prevents overlooked information due to synonyms, misspellings, abbreviations, etc. that thwart basic keyword search.
By scanning metadata, the AI can also identify relevant sources based on activity timeframes, data subjects associated, department, and other indicators matching the request scope. This overcomes reliance on matching specific content.
The system consults its knowledge base on where different data types are stored to guide the search. For instance, it knows to look in CRM systems for sales records, media servers for call center interactions, and HR databases for performance reviews. This domain-specific understanding prevents critical oversights.
Clients report AI reducing overall review sets by 60-90% compared to standard search methods by precisely pinpointing relevant data. This makes review feasible without missing key sources that could lead to sanctions.
One global bank said manual searches took months and still failed to capture all relevant personal data prior to AI. Despite extensive IT assistance, major gaps remained across legacy systems.
Intelligently pinpointing only appropriate data is key to crafting compliant DSAR responses efficiently. Without precision, organizations get lost in oceans of irrelevant content. With it, they can find exactly what they need, nothing more.
While data subjects have a right to their personal information, regulations like GDPR contain exemptions allowing certain data to be withheld from DSAR responses. Determining what merits exemption during review prevents over-disclosure, but is highly nuanced. Without proper assessment, organizations may expose sensitive data or intellectual property. Done manually, exemption review is complex and inconsistent. AI enables legally compliant, scalable application of exemptions.
A primary exemption covers data containing third party information. For instance, emails between the data subject and others, performance reviews containing peer feedback, or transaction records showing counterparty details. Isolating truly third party data requires understanding context like whether all participants consented to sharing in a group chat.
Organizations struggle reviewing high volumes consistently for third party data, especially unstructured content like communications. Small mistakes expose organizations to breach claims. Leading law firms use AI to reliably redact third party information across thousands of documents within hours.
Other common exemptions apply to data revealing trade secrets, legal privilege, or posing harm like exposing whistleblower identities. Assessing these exceptions depends on subtle contextual and semantic nuances within unstructured text. For example, determining whether an email chain discussing misconduct contains privileged legal advice requires deep comprehension.
Manually applying exemptions at scale leads to missed redactions or excessive disclosures. With advanced natural language processing, AI can parse meaning and make legally informed decisions on applying exemptions across vast datasets rapidly. Where uncertainty exists, it flags documents for human review.
By consulting its knowledge base on jurisprudence, regulations, and internal policies, the AI ensures consistent, compliant application of redactions. This interprets exemptions narrowly to avoid falling afoul of transparency requirements.
Clients have seen AI improve exemption assessment accuracy from 60% manual to over 90%, with organizations like UK insurers reducing breach risks. Law firms praise AI"s ability to remove privileged data at scale with minimal oversight, instead of lawyer time spent analyzing documents.
Properly redacting sensitive personal data is crucial for crafting compliant DSAR responses. However, the manual redaction process is enormously time-intensive and prone to human error that leaves organizations exposed. With fines reaching 4% of global revenue, mistakes redacting sensitive fields like financial information or medical history could prove disastrous.
Fortunately, AI is revolutionizing redaction by automating the most tedious, consistency-demanding aspects. Organizations can upload source documents and simply indicate fields requiring redaction like social security numbers or account details. AI can then rapidly black out matching text across thousands of pages with perfect accuracy.
Even unstructured data posing more complexity like account statements, medical forms, and emails containing a mix of sensitive and benign content can be automatically protected. AI understands context to isolate and redact only relevant fields, unlike broad blackout methods.
For example, an account statement containing transaction details alongside personally identifiable information can be parsed to redact only specific account numbers, leaving benign data intact for transparency. AI comprehends semantics to make selective redactions reflecting data sensitivity.
EY Switzerland credits its AI redaction engine with reducing the risk of inadvertent data leakage during DSAR fulfillment by over 80% compared to human process. It also accelerated response timelines by automatically handling the most laborious task.
AI provides flexibility to configure bespoke redaction rules and templates tailored to each organization"s data environment. Preconfigured patterns like email addresses or mobile numbers cover common scenarios, while custom fields can be added to protect organization-specific information like employee IDs, product codes, or internal project names.
Audit trails provide transparency into what content was redacted for investigatory purposes. And by handling redaction automatically, organizations reduce reliance on manual review where errors or oversight are more likely compared to consistent algorithms.
For highly regulated sectors like finance and healthcare, being able to rapidly redact sensitive information with guaranteed accuracy is critical. One bank reported AI reducing the compliance team effort required to finalize DSAR responses by over 70% compared to their previous manual process.
Delivering data in the required format is a critical final step in DSAR fulfillment that heavily impacts recipient experience. However, cobbling together exports from various sources into the requested delivery channel has posed a major pain point.
DSARs often require data to be provided through specific mediums, whether email, posted USB drives, customer portals, or hard copy printouts. Data sourced across the organization"s many repositories and systems must be consolidated into the stipulated format.
This involves extensive document preparation and delivery logistics. Staff must extract relevant data, standardize formats like PDF for portability, assemble records into any mandated organization like chronology, prepare indexes, and finalize outputs to the requested channel. This requires heavy lifting across teams.
Without automation, fulfilling requests via customer portals may demand IT staff convert data and upload to appropriate systems. Emailing data requires staff to download, format, and attach files to responses. Mailing printouts and USB drives involves document preparation, printing, postal processes, and managing security controls.
At each stage, nuances like confirming appropriate access permissions and encrypting data add complexity organizations struggle to manage. When relying on makeshift manual processes, misaligned outputs or delayed delivery could quickly violate compliance timeframes.
The UK Information Commissioner"s Office found 40% of organizations surveyed failed to provide DSAR data in the requester"s specified format. 15% refused to provide data digitally when required. This highlights common struggles adapting diversely sourced data into requested mediums.
AI automation provides a huge advantage by standardizing and routing data to required formats and delivery channels automatically. Once relevant data is identified, the AI handles converting it into compliant, accessible forms like PDF. Bulk outputs can be generated rapidly even from hard copy records via OCR.
AI manages transfer to the appropriate channel by auto-populating portals, attaching files to emails, generating printouts, or producing USB data sets. Integrations with payment systems even allow data delivery on approved purchase.
This eliminates the need for staff to manually piece together outputs and route to various destinations. What once took days of work across teams now happens seamlessly with AI automatically handling the heavy lifting.
Law firms have praised AI"s ability to collate DSAR data sourced from multiple clients and matters into a single, chronological export file. This integrated view saves recipients from reassembling records spread across individual communications and documents.
Sticking to mandated DSAR response timeframes poses a major stress point given manual processing bottlenecks. GDPR requires data to be provided within one month, with limited exceptions. This short window leaves little margin for delays while hunting down data, reviewing, redacting, and compiling responses across siloed systems.
A survey by the UK"s Information Commissioner"s Office found 31% of organizations missed DSAR deadlines due to underestimating resources required. 12% attributed delays to problems locating all relevant data. With GDPR permitting fines of up to 4% of global revenue for non-compliance, missed timeframes could catastrophically impact bottom lines.
Lacking automation, organizations resort to throwing more staff at DSARs as deadlines loom. But adding human bandwidth proves inefficient when key steps remain manual. Experts estimate 60-70% of DSAR processing time gets burned on repetitive tasks like data collection and document preparation versus value-adding review.
In contrast, AI automation offers exponential speed gains by handling the most tedious elements like data consolidation and formatting. This prevents time-sucking manual tasks from delaying responses. Leading law firms have reduced DSAR turnaround times from weeks to days using AI to eliminate repetitive human effort.
The Swiss insurer CSS reported slashing average DSAR response times from over a week to just 8 business hours using AI. By automatically processing key steps in the background, staff could focus on high-value review rather than playing catch up.
AI also helps prioritize DSARs based on mandated timeframes and request complexity to avoid breaching deadlines. Automated queuing and workflow management prevents simpler requests from getting stuck behind complex ones and ensures fulfilling requests chronologically.
Reminders can also be configured to promptly flag upcoming deadlines and remediate delays. For example, the AI may determine additional review bandwidth is required for a large pending request and notify managers days in advance to allocate resources and meet the timeframe. This proactive approach prevents fire drills.
By monitoring overall organizational DSAR workload, AI can help predict upcoming spikes in volume and recommend capacity expansion like added headcount. This allows maintaining compliant timeframes at scale versus being overwhelmed.
Legal tech pioneer Disco credits its AI-powered eDiscovery platform with the ability to "respond to regulatory investigations 85% faster". While aimed at litigation, similar time savings are achievable for DSARs by eliminating repetitive manual efforts.
Maintaining future DSAR compliance is crucial for organizations hoping to avoid repeated sanctions and permanently ingrain privacy practices. While using AI to optimize initial responses helps in the short term, truly minimizing risk requires learning from experience.
Organizations able to continuously improve their DSAR operations are best positioned for long-term compliance. As one privacy lawyer remarked, "The goal isn't just getting through today's requests, but building institutional knowledge so future requests become more seamless."
However, companies often struggle converting one-off DSAR experiences into strategic insights that prevent future issues. Post-mortems tend to lack rigorous analysis of what worked versus required improvement in areas like request processing, review efficiency, data protection, and response formatting.
Without methodically assessing successes and pain points after large DSAR volumes, the same challenges often resurface in subsequent cycles. Organizations find themselves stuck in a reactive loop, overwhelmed by each new wave of requests.
AI solutions offer powerful capabilities for analyzing DSAR workflows end-to-end to identify improvement opportunities. By digesting metrics on processing times, human reviewer activity, data obscurity, formatting errors, and more, AI can pinpoint where delays and compliance gaps originated.
Natural language processing reveals insights from freeform feedback provided by reviewers and requesters. This helps surface common issues that metrics alone may not capture, like confusion stemming from organization practices or communication gaps between teams.
By correlating metrics and feedback with factors like request complexity, data types, and organizational nuances, AI can determine root causes of inefficiencies. For instance, consistently slow processing for requests involving sales data may reveal gaps in CRM system search coverage.
These insights allow organizations to implement preventative measures like expanding available data sources, adjusting team responsibilities, introducing new review tools, and updating policies and procedures. This closes loopholes before they become repeat issues.
The privacy team at an international bank used AI analysis of past DSARs to uncover an overlooked legacy database containing customer marketing consent records. By integrating this into future search protocols, they remediated a systematic blind spot.
Erring on the side of transparency, some organizations even employ AI to redact and send requesters all data initially flagged as non-relevant during past searches. This proactive approach uncovers any data points that may have been mistakenly excluded from a response.
Through continuous improvement, AI allows organizations to scale DSAR operations smoothly over time rather than operating in crisis mode. Systemizers like Johnson & Johnson have developed extensive DSAR playbooks powered by insights from processing tens of thousands of global requests with AI assistance.
Other leaders like Microsoft augment in-house teams with on-demand expert AI resources from legal tech partners. By borrowing external AI capacity, they maintain oversight on spikes in DSAR volumes across regions and business units.