Case Studies

A global consumer products company with a distributed workforce needed to strengthen its information security posture. With sensitive intellectual property, regulatory obligations across multiple jurisdictions, and increasing use of Microsoft 365 collaboration tools, the security team sought a more resilient approach to protecting critical data against leakage, misuse, or unauthorized access. Challenge The existing environment lacked unified policies for sensitivity labeling, retention, and data loss prevention, making it difficult to enforce consistent governance across all business units. The client faced significant risks around: Data leakage from collaboration data in Microsoft Teams, SharePoint, and OneDrive. Lack of consistent data classification leading to overexposed sensitive content. Insufficient DLP controls for email and cloud-based sharing, creating regulatory and reputational risks. Growing compliance pressure across global operations, requiring alignment with GDPR, CCPA, and industry-specific regulations. Solution Lighthouse partnered with the client to design a comprehensive Microsoft Purview Information Protection and Data Loss Prevention (DLP) framework pilot that could scale globally. The solution included: This design provided the foundation for both proactive risk reduction and reactive incident handling. Results Through this engagement, the client achieved: Reduced risk of data exposure by applying consistent labeling and DLP rules across collaboration platforms. Improved regulatory compliance by aligning information protection policies with global privacy and industry frameworks. Enhanced incident visibility with reporting dashboards and adaptive policies that alerted security teams to high-risk events. Sustainable governance model enabling scalability as new collaboration tools and AI-driven workflows are adopted. Why It Matters As global enterprises accelerate digital collaboration, data security gaps in Microsoft 365 environments can create regulatory, financial, and reputational risk. By implementing a comprehensive governance and DLP framework, organizations can protect their most valuable assets: intellectual property, customer data, and regulated records, while enabling employees to work securely across borders. This project highlights how a well-designed information protection program, supported by Microsoft Purview, can simultaneously strengthen security and simplify compliance for multinational companies.

The most complex matters test outside counsel on every front with overwhelming data volumes, relentless deadlines, and multi-million-dollar outcomes on the line. Lighthouse is purpose-built for these moments. By uniting innovative AI with award-winning expertise, we give outside counsel the speed, precision, and confidence required for the most demanding matters, from high-stakes bankruptcies to sprawling multidistrict litigation and everything in between. So, when a massive second request hit with unforgiving timelines and zero margin for error, our team was ready. Here’s how we delivered speed, precision, and $20M in savings when it mattered most. Proven Performance Under Pressure The Challenge 30+ TB of data 2-month deadline A parallel private antitrust litigation The Lighthouse Advantage: AI + Expert Operations Lighthouse leveraged our proven approach for complex, high stakes matters: advanced AI integrated with skilled operations, review, and subject knowledge expertise: AI at the Core: $11.9M saved on review and production costs Lighthouse’s predictive large language models (LLMs) narrowed the responsive set by over 2M documents and helped reduce privilege review by 45%. Our generative AI then accurately drafted more than 100K privilege log entries, and identified and normalized 15K names and titles. $4.8M saved on key document identification Our experts used AI modeling to quickly surface documents that posed an antitrust risk, surfacing the 270 most important documents out of 4.4M documents in just three weeks. This work eliminated the need for traditional key document searches and issue tagging. No Rework, No Waste $3.5M saved on cross-matter work To support the parallel antitrust litigation, Lighthouse built a secure repository for cross-matter work product reuse and used AI to repurpose work across 680K documents. This work drove consistency and minimized duplicative review between the two matters. Operational Execution: 2 months Scaled and managed a 300-person review team Processed 30TB of data Produced 10TB+ and 20M+ images—error free Implemented a custom workflow to link M365 cloud attachments Why It Matters Second requests are a stress test, but they’re not the only time attorneys face massive data and unforgiving deadlines with millions of dollars at stake. When the pressure is highest, Lighthouse delivers. With AI, expertise, and operational discipline, we give outside counsel what they need to handle every complex matter.

Challenge Antitrust regulators issued a broad, high-stakes HSR Second Request to investigate a global company’s high-profile acquisition. To comply, the company and their outside counsel were faced with analyzing 30+ TB of data in less than a month.Solution  Lighthouse developed an AI driven approach powered by Lighthouse’s proprietary Large Language Models (LLMs) to eliminate relevance review, substantially reduce privilege review, perform privilege logging and assemble the names list, and identify key documents to mitigate risk without linear review.Simultaneously, Lighthouse operational teams executed multiple custom workflows to process and produce over 10TB+ of data and 20M+ images in 3 weeks—flawlessly.Their work included building a custom linking workflow for M365 cloud attachments and a secure data repository for work product reuse in a related antitrust litigation, while ramping a 300-person linear review team via Lighthouse’s Managed Review solution to meet the aggressive production deadline.Lighthouse AI Savings & ROI Breakdown Overall, using Lighthouse AI saved $20M+ with the following workflows: TAR powered by Lighthouses AI proprietary LLMsPrivilege identification powered by Lighthouse AI proprietary LLMsPrivilege log and names list generation via Lighthouse AI proprietary LLMsKey document identification powered by linguistic modeling and AIJunk file analysisCross matter analyticsHighlights of each of these workflows and the associated ROI are below.  Lighthouse’s Proprietary Predictive AI for Relevance: 40% Narrower Responsive Set Than Other TAR Tools Counsel only needed to review around 4,000 documents to stabilize and validate a TAR model backed by Lighthouse’s predictive LLMs that measured 85.91% precision at 76.49% recall. Lighthouse’s predictive AI model for relevance is shown to deliver a 30-40% smaller and more accurate R-set based upon comparative bake-offs. Had Lighthouse implemented a Second Request workflow using commercially available TAR tools, we would have likely seen a much broader responsive set, thereby requiring additional privilege review and production.Specifically, if Lighthouse had used traditional TAR tools, it would have resulted in an estimated additional 2M documents in scope for production, translating into approximately $5.8M in additional privilege review costs and $400,000 in added production expenses—costs avoided by using Lighthouse Responsive AI.Separately, Lighthouse’s Review Management team performed Junk File analysis and identified that around 95,000 documents out of the more than 860,000 non-TAR eligible documents were highly likely to be junk and could undergo a sampling workflow instead of a full linear review.Privilege Review: 45%+ Reduction  Lighthouse built an AI model for privilege that both removed documents from privilege review and accelerated the remainder. Using a tiered approach, counsel determined that around 780,000 documents (out of the more than 1.7M eligible for privilege review) could be removed from privilege review without linear review because they fell below the cutoff score and were unlikely to be privileged.Once the privileged documents were identified, Lighthouse’s generative AI was used to create first-pass privilege descriptions for the more than 100,000 documents on the privilege log. This removed the need for human drafting of log lines. As a result, the log required an investment of mere hours as opposed to days and the heavy expensive of a full contract attorney review and outside counsel QC.Lighthouse also used generative AI to build the names list for the privilege log. Lighthouse’s AI model quickly analyzed around 130,000 documents to identify and provide close to 15,000 normalized names with titles for the privilege log. Key Document Identification The Lighthouse AI team used modeling to quickly surface documents that could pose an antitrust risk to the company. This process eliminated the need for the more dated approach of search+linear review for key documents and issues tags. Out of an initial tranche of 4.4M documents, the Lighthouse team identified roughly 270 documents of the greatest interest and sensitivity and delivered them to counsel in 5 deliveries over the course of just 3 weeks.To provide additional support to counsel, the Lighthouse AI team also categorized the final Responsive document set based on risk profile—classifying a broad set of documents as Likely Risky and Likely Safe over the course of just 1.5 weeks.Work Product Reuse for Cross-Matter EfficiencyThe Second Request had a large set of overlapping data with a concurrent antitrust litigation. To ensure there was no duplicative review and to drive consistency, Lighthouse built a secure data repository that enabled work product reuse between this Second Request and that concurrent litigation and used Lighthouse AI to drive cross matter analytics. With Lighthouse AI, Lighthouse repurposed calls for around 680,000 documents, resulting in a savings of $3.5M between first pass review, 1L QC, and outside counsel QC.Speed, Quality, and Operational Delivery  The scale and complexity of this Second Request required an extraordinary cross-functional effort across several Lighthouse Client Services and Operational teams who worked tirelessly to deliver a seamless, high-quality result under immense time constraints. The core of this matter was completed in under 60 days, demonstrating an exceptional level of execution.Key highlights of the operational delivery include: High-Volume Processing and Custom Workflows: Lighthouse processed over 30TBs of data, ensuring rapid ingestion, indexing, and AI-powered classification. A custom workflow was implemented to link M365 cloud attachments, meeting regulatory requirements.Flawless Data Production at Scale: Within 3 weeks, the Lighthouse team produced over 10TBs of data and more than 20M images, achieving a 100% error-free production result. This ensured compliance without delay or rework.Scalable Review Team for Complex Work Streams: Leveraging our Managed Review capabilities, Lighthouse rapidly scaled a linear review team to 300 professionals. The team expertly navigated multiple complex work streams, dynamically segmenting data to mitigate risk while accelerating the review process to meet the production deadline. The bulk of review ramped and concluded in a mere 4 weeks. This work showcases the coordination, detail, operational excellence and sheer dedication of the Lighthouse team in delivering a timely, high-quality outcome in an intense regulatory investigation.

The ChallengeA major media company received a Letter of Inquiry (LOI) from the FCC, triggering a high-stakes regulatory investigation. The company was required to produce relevant communications within a month—but two weeks in, the legal team still needed to collect over 2 million documents from 16 custodians. Complicating matters further, the company’s software platform was mid-transition, raising serious concerns about data integrity and reporting reliability. The SolutionRecognizing the urgency and complexity of the matter, the media company and its outside counsel turned to Lighthouse. With an immense volume of documents, looming regulatory deadlines, and a technology transition in progress, they needed more than linear review—they needed a strategic partner with forensic, eDiscovery project management, and AI expertise. Within just four days, Lighthouse’s forensics experts collaborated with the company to collect and process all relevant custodian data, including associated family files. From there, our project management team worked with the company and its counsel to apply targeted filtering—focusing on communications between key senders and recipients. This reduced the original 2 million documents to a refined universe of 94,000. Using advanced email threading and junk file analysis, the team further reduced the review set to 59,000 documents. Given the aggressive timeline, volume of documents, and the dataset’s low privilege risk, Lighthouse consultants recommended deploying Relativity aiR for Review. Working closely with inhouse and outside counsel, Lighthouse developed a defensible AI review prompt using an iterative sampling workflow designed to meet stringent recall standards and maximize precision. Only 300 documents were reviewed during this iterative phase. Relativity aiR identified a predicted responsive universe of 28,000 documents. A first-level review was completed in just five days, followed by a quality control review conducted by outside counsel. Final validation confirmed 88% recall and 96% precision—exceeding regulatory and eDiscovery defensibility standards. The ResultsUltimately, 18,000 documents were successfully produced on time, along with an expert declaration on the defensibility of the process from a Lighthouse Strategic Consultant. When the FCC issued a supplemental request, the teams were able to use the aiR-powered workflow once again to quickly review 2,000 additional documents—resulting in the production of the 300 relevant files.

In a high-stakes matter, a Fortune 500 CPG company and its Am Law 200 counsel faced a common eDiscovery challenge with uncommon complexity: a constantly changing review population.The ChallengeAt the heart of this large-scale matter was a significant complication: scope volatility. The Technology-Assisted Review (TAR) universe had to be redefined and recalibrated—multiple times. For most providers, this would derail consistency, timing, and cost.The Solution Lighthouse leaned into the complexity. Using a modular AI framework, we built workflows that could flex in real time; adapting to evolving populations without losing precision. Key solutions included:TAR re-scoping at scale of a 4.6M document universe without destabilizing the model Automated tracking and updates to full-family inclusions and exclusion logic Integrated PII, privilege, and image AI analysis to ensure comprehensive review coverageThe Result Despite the volatility, Lighthouse delivered enterprise-grade performance: 76% precision at 75% recall and the required documents were produced on time. What could have been a fractured and inefficient review was a streamlined effort due to Lighthouse’s ability to manage dynamic review workflows using AI. In today’s litigation and investigation landscape, moving targets are the norm. Lighthouse delivers the consistency, agility, and accuracy needed to keep pace and produce with confidence.

Client: Global academic medical system Stakeholders: CISO, Information Governance, Legal Tech stack: Microsoft 365 + Microsoft Purview (SharePoint, OneDrive, Exchange, Teams) Objective: Identify, label, and protect high‑value IP across M365 Business Challenge Conventional pattern matching missed nuanced research content; labels were inconsistent. Emerging IP taxonomy lacked consistent, enforceable labels across repositories. Conventional pattern matching couldn’t reliably detect unstructured, nuanced IP. Teams needed clarity on when to use Sensitive Info Types (SITs), Exact Data Match (EDM), and Trainable Classifiers, and how to govern them. Wanted to compare outcomes with prior third‑party classifiers. What Lighthouse Did IP Taxonomy + Purview Labels Mapped proprietary IP categories to a label set Blended Classifier Strategy Combined SIT, EDM, and Trainable Classifiers Operationalize Piloted and tuned models aligned with retention/legal hold/eDiscovery, with auto‑labeling and user prompts. Controls Developed change‑management materials Outcomes Common IP Language: Agreed taxonomy mapped to enforceable labels. High‑Confidence Detection: Trainable classifiers surfaced custom IP Consistent Protection: High‑value content auto‑labeled with policy‑driven controls in M365. Governed Workflows: Clear guidance on SIT vs EDM vs Trainable; fewer false positives/negatives; faster to eDiscovery. Timeline Weeks 0–1 — Kickoff + Plan Weeks 2–4 — Design + Setup Weeks 5–9 — Run Pilots Weeks 10–11 — High Level Design + Training Why Microsoft Purview for Data Protection Enterprise-wide strategy - Unified data security, governance, compliance Integrated governance - DLP, retention, legal hold, eDiscovery Flexible detection models - Sensitive Info Types, Exact Data Match, Trainable Classifiers Persistent, label-based protection - Embedded permissions travel with data

When a Fortune 500 CPG company faced a second request tied to a business divestiture, its Am Law 200 counsel partnered with Lighthouse to manage the heavy data lift of 4.6M docs. With tight deadlines, shifting custodians, and complex privilege concerns, the stakes were high—and consistency was critical.The ChallengeVolatile TAR populations and changing custodian scopes Compressed timelines typical of second requestsHigh data volume across diverse file types including video and image The Solution Lighthouse deployed a suite of AI-powered tools to keep the matter on track: AI-driven TAR and privilege/PII detection to streamline review Generative AI + transcription tech enabled fast image/video analysis Modular workflows allowed rapid pivots as scope evolved The Result The AI-led approach let attorneys focus on strategy while Lighthouse handled operational complexity—proving the power of collaborative, AI-enabled review. The matter was completed on time and to the satisfaction of all parties involved. The legal team met their regulatory deadlines, and the CPG client was well-positioned for the next stages of its divestiture. Why It Matters This engagement shows how scalable AI, smart workflows, and strong partnerships can turn regulatory chaos into consistent, repeatable success.

Key ResultsDocument set reduced from 750,000 to 158,000 documents Review speed increased from 10 to up to 60 docs/hour from bulk issue coding AI privilege model trained for reuse, improving speed and consistency 310 key documents identified across six core topics Production delivered with confidence, accuracy, and defensibility The Challenge: A Sweeping Request and a Tight Timeline At the outset, the FTC issued an expansive request that pulled in more than 750,000 records including emails and Slack messages. Agreement on keywords proved difficult, as regulators pushed for maximum disclosure.Lighthouse supported outside counsel through nine rounds of negotiation, including line-by-line responses to regulator objections, landing on a refined population of 158,000 documents—an 80% reduction in review scope. Review at Scale: Fast, Focused, and Credible One immediate challenge with prioritized document review for this matter was the large number of issue codes. Previously, similarly complex issue codes had slowed review to just 10 documents per hour. Moreover, outside counsel expected the FTC to heavily scrutinize the issue code distribution, making accuracy and nuance critical. To guide reviewers away from default or overly broad issue codes, a classifier was used to apply issue codes to responsive documents. Three refinement cycles ensured the issue codes were applied accurately, and as counsel directed. This method not only passed regulator scrutiny, review was accelerated by 4.5-6x. Key Document Identification and Proactive QC In parallel to review, Lighthouse began 4 rolling deliveries of key documents supporting six topics. Over four weeks, the team surfaced a highly-curated set of 310 documents to support case strategy. The work to identify key documents was also used to identify documents that were likely under-coded by contract reviewers—enabling the team to course-correct in real time. Building an AI Privilege Model for This Matter—and the Next The team next trained an AI privilege model to support privilege review, not just for this matter, but for future matters as well. The process began with a linguistically curated sample set, which counsel coded with future applicability in mind. The trained AI privilege classifier analyzed documents, delivering outputs in Relativity, allowing reviewers to reference privilege scores as part of their decision-making. Technical Execution: Overcoming Real-World Complexity Behind the scenes, Lighthouse had to address several technical hurdles. The review was hosted in RelativityOne, and initial data connectors required custom adjustments to work with the client’s Slack and metadata structure. In particular, Lighthouse developed a workaround to solve for Slack transcripts, which didn’t have file extensions, thus breaking standard ingestion processes.

The ChallengeIn a high-stakes regulatory investigation for a heavily regulated client, outside counsel faced a massive task: produce a 200,000-entry privilege log with specificity, consistency, and speed—without drawing regulatory fire. The Lighthouse SolutionLighthouse’s GenAI-powered privilege log solution gave outside counsel something neither traditional privilege log methods or other GenAI privilege log solutions could: customized, defensible entries at scale—with almost no manual drafting by review teams or outside counsel. What set it apart? The human-in-the-loop model. Lighthouse AI experts partnered with the legal team to fine-tune outputs for that matter’s unique needs, iterating on key components of each log line until the results were exactly right. Why It WorkedRather than force-fit the log into one-size-fits-all templates or get stuck with GenAI outputs that weren’t a good fit for this very unique matter, Lighthouse tailored and continuously iterated our GenAI prompting across three key dimensions: Document Type (e.g., Email, Memo) Legal Hook (Why the document is privileged e.g., “requesting legal advice”)Subject Matter (What the privileged information is generally regarding)How We Did ItOutside counsel needed subject matter descriptions to be as specific as possible without giving away privilege content, while creating uniformity across the document type and legal hooks based on the unique documents at issue in the investigation. Through prompt engineering and targeted feedback with outside counsel, the AI’s first draft evolved from raw potential to precision output that exactly met their needs:100+ document types condensed to a consistent set of 9 that outside counsel needed ‍80+ legal hooks refined down to 8, specific to the way attorneys worked at the underlying company ‍120,000+ unique Re: line descriptions that explained specific legal projects and work, none reused more than 1% of the time The Results200K+ log entries generated on a tight timeline No vague or red-flag phrasing—achieved by iterating with GenAI prompts to ensure that negative language caught in QC was removed across the entire privilege log Near-zero manual drafting by the legal team Custom configurations (e.g., suppressing references to specific entities) tailored to client preferencesFocused QC efforts where it mattered—resulting in massive time and cost savingsThe ROIOutside counsel’s job? Review, not write. By shifting their time to targeted QC and feedback instead of manual drafting, the legal team met their deadline under pressure—and under budget.

Company Overview A global Fortune 500 manufacturing company with facilities and offices across North America, Latin America, Europe, Asia, and Australia.ChallengesThe company was evaluating Microsoft Purview E5 licenses to support its information protection, insider risk management, data loss prevention, and sensitivity labeling goals. The data protection team needed to validate the efficacy of the Purview tools when applied to company data within their M365 environment. The data project leader shared a list of use cases to test against the platform’s risk identification and alert capabilities. SolutionLighthouse’s consultants designed and ran a four-month pilot to test Microsoft Purview’s sensitivity labeling, Data Loss Prevention (DLP), Insider Risk Management (IRM), and Defender for Cloud Apps capabilities across the client’s live Microsoft 365 environment. The team configured and validated more than 10 distinct data protection policies, including global personal information labels for Exchange, Teams, OneDrive, and endpoint devices. The project included six sensitive information types (such as PII, PCI data, and passport numbers), and piloted risk-based alerts for questionable user and departing employee behavior. Lighthouse developed a detailed Report Card and Recommendations Report, delineating a clear path for full implementation of validated controls company-wide. Key OutcomesDuring the initial four-month engagement, Lighthouse’s experts successfully validated each use case, demonstrating that Microsoft Purview E5 was indeed the correct tool for the client’s data protection needs. The client purchased Purview E5 licenses and engaged Lighthouse to guide the full implementation of all the capabilities we had piloted. By following the guidance of Lighthouse data experts, the company’s data protection team developed an ongoing, flexible data protection strategy and program which mitigated multiple risks by automating data classification, labeling, and user notifications. Lighthouse helped this client accelerate its data protection maturity model and establish best practices, workflows, and classifiers to strengthen data privacy and security. We can do the same for you. Contact an expert to get started.

During the first trial of a high-stakes, serialized product liability case, a legal team was stunned when opposing counsel introduced damaging documents that had originated from the client’s own production. Despite months of review by both in-house and outside counsel, these documents had slipped through the cracks—threatening to derail the case and set a dangerous precedent for future litigation.With millions at stake, they needed a solution—and fast. They turned to Lighthouse. In just three days, a Lighthouse expert dug into the 25,000 documents in the production, uncovering 14 crucial rebuttal documents that counsel used at trial to challenge the plaintiffs’ claims.This swift, targeted response set the client up for success and in a position to save the company millions of dollars in judgments and protect them from similar challenges in upcoming cases.In the world of serialized product liability litigation, where one case can impact many others, the power of rapid, focused eDiscovery can be the difference between a costly loss and a decisive win.

SolutionThe Director of Information Security partnered with Lighthouse to conduct a comprehensive scan using Lighthouse’s proprietary environment scan technology and Microsoft Information Protection (MIP). This scan could locate sensitive data across the enterprise and provide the necessary visibility to roll out full MIP policies.1. Lighthouse’s Comprehensive Environment Scan Lighthouse’s scan helped identify and locate sensitive data, helping the security team to understand its exposure and design its protection strategy. An example of findings included:Teams: /LegacyRightAngleData contained 139,000+ instances of sensitive data. SharePoint: /Financial_DMS stored 52,000+ instances of sensitive data. OneDrive: /[single employee] held 18,900+ instances of sensitive data. Most Common Sensitive Data TypesABA Routing NumbersEU Passports NumbersSWIFT CodesU.K. National Health identifiers2. Created Sensitivity Labels in Pilot Mode Following the scan, Lighthouse supported the security team in developing sensitivity labels in pilot phase, including: Testing Auto-Labeling & Classification: Defining initial label rules based on scan results. Evaluating Impact Before Full Rollout: Assessing how sensitivity labels functioned across departments and workflows.Preparing for Future Policy Implementation: Establishing a structured data protection strategy before MIP policies were fully deployed. Key Outcomes The Lighthouse environment scan gave the organization critical visibility into sensitive data locations, laying the groundwork for stronger data governance, protection, and compliance. Critical Visibility for Future Protection: Identified where sensitive data resided to guide security and governance efforts. Pilot Sensitivity Labeling Program: Launched sensitivity labels to test the efficacy of policies and refine data governance practices. Foundation for MIP Rollout: Positioned the team to automate protection and enforce compliance through Microsoft Purview. The Lighthouse environment scan helped the client uncover hidden risks and build a foundation for stronger data governance. With clear visibility and a pilot labelling program, the organization is prepared to advance its Microsoft Purview rollout and reduce exposure.

The ChallengeRecently, the U.S. Department of Justice (DOJ) issued a broad and urgent HSR Second Request in connection with a high-profile merger for a large, highly-regulated corporation. The regulatory inquiry required fast, defensible data collection from a range of custodians, many of whom were senior executives. With just weeks to act, the stakes were clear: respond efficiently and thoroughly or risk delaying the transaction’s approval.The request included nearly 30 custodians spread across the U.S., many with privacy sensitivities around their mobile data.The SolutionLighthouse assembled a cross-functional team of digital forensics experts and client services professionals to lead a high-touch, high-urgency workflow. Coordination between the digital forensics project manager and client services project manager ensured that collections, handoffs, and processing moved forward without bottlenecks—driven by daily alignment and real-time communication.Over six weeks, Lighthouse collected mobile data from all 27 custodians using a mix of remote and on-site methods, all handled in-house to minimize disruption and maintain control. The team leveraged industry-standard tools and proprietary workflows to extract encrypted messaging data from apps like WhatsApp and Signal, even when on-site collection was required. To address privacy concerns, Lighthouse implemented a workflow where custodians approved contact lists before any messages were filtered and prepared for review. This approach ensured rapid turnaround—often within one business day—without compromising data integrity or custodian trust.ResultsBy strategically splitting collections between remote and on-site, the Lighthouse team accelerated the project, completing collection in just 1.5 months and saving an estimated 60 hours of work time. More importantly, the client was able to respond to the DOJ within deadline—and was armed with complete, accurate, and defensible data drawn from even the most sensitive mobile sources.