Facing the Challenge: Data Classification Without Centralization
Legal ops teams, records managers, and compliance leads often hit a roadblock: their information governance projects stall due to the costs and logistics of moving data into a central platform for classification. This leads to incomplete data analysis, and when audits or breaches occur, the gaps become evident.
The following questions explore the potential of in-place classification, particularly AI-driven methods that analyze data where it resides. Traditional eDiscovery workflows often promise comprehensive analysis but result in long timelines and high costs. Teams seeking proactive governance programs frequently encounter scalability issues that make traditional methods prohibitively expensive.
Q1: "Can AI classify data without collecting it first? How does that work?"
Yes, it can. Traditional models require data collection into a centralized platform for classification, leading to high costs and delays. In-place classification reverses this process. Instead of moving data to the AI, classification models are deployed through a distributed architecture that processes content near its source. This creates micro-indexes for individual mailboxes, OneDrive accounts, SharePoint sites, and file shares. AI models classify the content associated with each micro-index, and the classifications are written back as searchable attributes.
While compute and storage costs remain, the need to transfer, duplicate, and centrally host the entire data set is eliminated. Only data requiring further review or remediation is moved to downstream systems.
Q2: "Is in-place classification cheaper than traditional methods?"
It likely is, though savings depend on your data volume and repository mix. Traditional workflows incur costs for data transfer, ingestion, processing, hosting, and storage fees. These costs quickly escalate across large data volumes.
In-place classification uses existing distributed indexes for enrichment, avoiding the need to duplicate your entire data estate into a separate platform. You're not paying for monthly hosting fees on a second copy of everything. Compute and storage requirements are distributed across infrastructure designed for enterprise scale, not concentrated in a single processing environment.
For PII remediation, you'd classify across file shares, identify files containing PII, and only collect those files for review, avoiding the need to move the entire file-share estate first.
Q3: "Does in-place classification avoid Microsoft 365 throttling limits?"
Yes, because you're not collecting large volumes from Microsoft 365 upfront. Microsoft imposes service-protection limits to prevent excessive API resource consumption. Large-scale collections can hit these throttles, causing delays.
In-place classification creates micro-indexes for each mailbox or site without triggering throttling. The work is distributed, limiting the load on any single API endpoint. Once classification is complete, you know which items contain regulated content or responsive data, allowing for a smaller, faster collection process that avoids throttling.
This is crucial for GDPR or CCPA data-subject requests, where regulatory deadlines require prompt data identification.
Q4: "Is comprehensive analysis still impractical?"
Not necessarily. Sampling has been a workaround due to the prohibitive cost of comprehensive classification at scale. You'd sample a subset, extrapolate findings, and accept the risk of non-representative samples. For routine matters, this might suffice, but not for breaches or audits.
In-place classification changes this. You can apply consistent classification policies across your enterprise without collecting everything first. The work is distributed across infrastructure that can handle the scale.
Whether comprehensive analysis suits your needs depends on your risk tolerance and business objectives, but it's no longer constrained by technical or economic limitations.
Q5: "How does this help with records retention?"
Records retention often fails because organizations can't identify their data. A Records Control Schedule might dictate retention periods, but finding and classifying records scattered across mailboxes and file shares is challenging.
In-place classification allows you to discover and tag content according to your retention schedule without moving everything into a records management system. You classify distributed repositories, identify documents matching retention categories, and use classifications to drive policy-based actions, like applying retention labels or identifying redundant data for disposal.
This supports event-based retention triggers, enabling you to manage records related to contract terminations or employee departures effectively.
Q6: "Can in-place classification run continuously for proactive governance?"
Yes, it can. Unlike traditional workflows, micro-indexes can be updated independently and in parallel, allowing classification to be part of scheduled or incremental updates. You're not relying on a single snapshot but maintaining an ongoing understanding of your data.
This is vital for governance programs monitoring policy violations, data minimization, or maintaining audit-readiness. You can continuously detect unauthorized PII locations, inappropriate sharing of privileged material, or data retention beyond policy limits.
The shift to proactive governance has been the goal, and distributed, continuously updated classification supports this at enterprise scale without constant recollection and reprocessing.
Next Steps
If you're considering in-place classification, focus on three key questions: Does the architecture support distributed micro-indexes or require centralized aggregation? Can classifications be updated incrementally, or does every update need full reprocessing? How do data movement and duplication costs change as your data estate grows?
For GDPR or CCPA compliance, examine how the system handles data-subject requests and deletion obligations across distributed repositories. For records programs, assess how classifications align with your Records Control Schedule and whether the system supports event-based retention triggers.
The technical architecture is crucial for operating at enterprise scale without resorting to sampling or accepting unseen gaps.
