How to Protect Sensitive Data in Azure

One of the most interesting blind spots I see in mature security programs isn’t a database or a SaaS app. It’s web archives.

If you’re in financial services, you may be required to archive every version of your public website for years. Legal teams preserve web content under hold. Marketing and product teams crawl competitors for competitive intel. Security teams capture phishing pages and breach sites for analysis. All of that activity produces WARC and ARC files - standard formats for storing captured web content.

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Now ask yourself: what’s in those archives?

Where Web Archives Come From and Why They Get Ignored

In most enterprises, web archives are created in predictable ways, but rarely treated as data stores that need to be actively managed. Compliance teams crawl and preserve marketing pages, disclosures, and rate sheets to meet record-keeping requirements. Legal teams snapshot websites for e-discovery and retain those captures for years. Product and growth teams scrape competitor sites, pricing pages, and documentation, while security teams collect phishing kits, fake login pages, and breach sites for analysis.

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All of this content ends up stored as WARC or ARC files in object storage or file shares. Once the initial crawl is complete and the compliance requirement is satisfied, these archives are typically dumped into an S3 bucket or on-prem share, referenced in a ticket or spreadsheet, and then quietly forgotten.

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That’s where the risk begins. What started as a compliance or research activity turns into a growing, unmonitored data store - one that may contain sensitive and regulated information, but sits outside the scope of most security and privacy programs.

What’s Really Inside a WARC or ARC File?

A single WARC from a routine compliance crawl of your own site can contain thousands of pages. Many of those pages will have:

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• Customer names and emails
• Account IDs and usernames
• Phone numbers and mailing addresses
• Perhaps even partial transaction details in page content, forms, or query strings

If you’re scraping external sites, those files can hold third‑party PII: profiles, contact details, and public record data. Threat intel archives may include:

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• Captured credentials from phishing kits
• Breach data and exposed account information
• Screenshots or HTML copies of login pages and portals

Meanwhile, the archives themselves grow quietly in S3 buckets and on‑prem file shares, rarely revisited and almost never scanned with the same rigor you apply to “primary” systems.

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From a privacy perspective, this is a real problem. Under GDPR and similar laws, individuals have the right to request access to and deletion of their personal data. If that data lives inside a 3‑year‑old WARC file you can’t even parse, you have no practical way or scalable way to honor that request. Multiply that across years of compliance archiving, legal holds, scraping campaigns, and threat intel crawls, and you’re sitting on terabytes of unmanaged web content containing PII and regulated data.

Why Traditional DLP and Discovery Can’t Handle WARC and ARC

Most traditional DLP (Data Loss Prevention) and data discovery tools were designed for a simpler data landscape, focused on emails, attachments, PDFs, Office documents, and flat text logs or CSV files. When these tools encounter formats like WARC or ARC files, they typically treat them as opaque blobs of data, relying on basic text extraction and regex-based pattern matching to identify sensitive information.

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This approach breaks down with web archives. WARC and ARC files are complex container formats that store full HTTP interactions, including requests, responses, headers, and payloads. A single web archive can contain thousands of captured pages and resources: HTML, JavaScript, CSS, JSON APIs, images, and PDFs, often compressed or encoded in ways that require reconstructing the original HTTP responses to interpret correctly.

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As a result, legacy DLP tools cannot reliably parse or analyze WARC and ARC files. Instead, they surface only fragmented data such as headers, binary content, or partial HTML, without reconstructing the full user-visible context. This means they miss critical elements like complete web pages, DOM structures, form inputs, query strings, request bodies, and embedded assets where sensitive data such as PII, credentials, or financial information may exist.

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The result is a significant compliance and security gap. Web archives stored in WARC and ARC formats often contain regulated data but remain unscanned and unmanaged, creating a persistent blind spot for traditional DLP and DSPM programs.

How Sentra Scans Web Archives at Scale

We built web archive scanning into Sentra to make this tractable.

Sentra’s WarcReader understands both WARC and ARC formats. It:

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• Processes captured HTTP responses, not just headers
• Extracts the actual HTML page content and associated resources from each record
• Normalizes those payloads so they can be scanned just like any other web‑delivered content

Once we’ve pulled out the page content and resources, we run them through the same classification engine we apply to your other data stores, looking for:

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• PII (names, emails, addresses, national IDs, phone numbers, etc.)
• Financial data (account numbers, card numbers, bank details)
• Healthcare information and PHI indicators
• Credentials and other secrets
• Business‑sensitive data (internal IDs, case numbers, etc.)

Because WARC files can be huge, we do all of this in memory, without unpacking archives to disk. That matters for two reasons:

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1. Performance and scale: We can stream through large archives without creating temporary, unmanaged copies.
2. Security: We avoid writing decrypted or reconstructed content to local disks, which would create new artifacts you now have to protect.

We also handle embedded resources - images, documents, and other files captured as part of the original pages — so you’re not only seeing what was in the HTML but also what was linked or rendered alongside it. Sentra’s existing file parsers and OCR engine can inspect those nested assets for sensitive content just as they would in any other data store.

Bringing Web Archives into Your DSPM Program

Once you can actually see inside web archives, you can bring them into your data security program instead of pretending they’re “just logs.”

With Sentra, teams can:

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• Discover where web archives live across cloud and on‑prem (S3, Azure Blob, GCS, NFS/SMB shares, and more).
• Classify the captured content for PII, PCI, PHI, credentials, and business‑sensitive information.
• Assess regulatory exposure from long‑running archiving programs and legal holds that have accumulated unmanaged PII over time.
• Support DSAR and deletion workflows that touch archived content, so you can respond to GDPR/CCPA requests with an honest inventory that includes historical web captures.
• Evaluate scraping and threat‑intel collections to identify sensitive data they were never supposed to capture in the first place (for example, credentials, breach records, or third‑party PII).

In practice, this often leads to concrete actions like:

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• Tightening retention policies on specific archive sets
• Segmenting or encrypting archives that contain regulated data
• Updating crawler configurations to avoid collecting sensitive content going forward
• Aligning privacy teams, legal, and security around a shared understanding of what’s actually in years’ worth of WARC/ARC content

Web Archives Are Data Stores - Treat Them That Way

Web archives aren’t just compliance artifacts, they’re data stores, often holding sensitive and regulated information. Yet in most organizations, WARC and ARC files sit outside the scope of DSPM and data discovery, creating a blind spot between what’s stored and what’s actually secured.

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Sentra removes that tradeoff. You can keep the archives you’re required to maintain and gain full visibility into the data inside them. By bringing WARC and ARC files into your DSPM program, you extend coverage to web archives and other hard-to-reach data—without changing how you store or manage them.

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Want to see what’s hiding in your web archives? Explore how Sentra scans WARC and ARC files and uncovers sensitive data at scale.

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Ask any security engineer how they feel about DLP alerts and you’ll usually get the same reaction. They are drowning in them. Over the last decade, DLP has built a reputation for noisy alerts, rigid rules, and confusing dashboards that bury real risk under a mountain of “maybe” events.

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Teams roll out endpoint, email, and network DLP, wire in SaaS connectors, and import standard PCI/PII templates. Within weeks, analysts are triaging hundreds of alerts a day, most of which turn out to be benign. Business users complain that normal work is blocked, so policies get carved up with exceptions or quietly disabled. Meanwhile, the most sensitive data quietly spreads into collaboration tools, cloud storage, and AI workflows that DLP never sees.

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The problem is that DLP is being asked to do too much on its own: discover sensitive data, understand its business context, and enforce policies in motion, all from a narrow view of each channel. To fix false positives in a durable way, you have to stop treating DLP as the brain of your data security program and give it an actual data-intelligence layer to work with.

That’s the role of modern Data Security Posture Management (DSPM).

Why Traditional DLP Can Be So Noisy

Most DLP engines still lean heavily on pattern matching and static rules. They look for strings that resemble card numbers, social security numbers, or keywords, and they try to infer “sensitive vs. not” from whatever they can see in a single email, file, or HTTP transaction. That approach might have been tolerable when most sensitive data sat in a few on‑prem systems, but it doesn’t scale to multi‑cloud, SaaS, and AI‑driven environments.

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In practice, three things tend to go wrong:

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First, DLP rarely has full visibility. Sensitive data now lives in cloud data lakes, SaaS apps, shared drives, ticketing systems, and AI training sets. Many of those locations are either out of reach for traditional DLP or only partially covered.

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Second, the rules themselves are crude. A nine‑digit number might be a government ID, or it might be an internal ticket number. A CSV export might be an innocuous test file or a real production dump. Without a shared understanding of what the data actually represents, rules fire on look‑alikes and miss real exposures.

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Third, each DLP product, the endpoint agent, the email gateway, the CASB, tries to solve classification locally. You end up with inconsistent detections and competing definitions of “sensitive” that don’t match what the business actually cares about. When you add those up, it’s no surprise that false positives consume so much analyst time and so much political capital with the business.

How DSPM Changes the Equation

DSPM was designed to separate what DLP has been trying to do into dedicated layers. Instead of asking DLP to discover, classify, and enforce all at once, DSPM owns discovery and classification, and DLP focuses on enforcement.

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A DSPM platform like Sentra connects directly, via APIs and in‑environment scanning, to your cloud, SaaS, and on‑prem data stores. It builds a unified inventory of data, then uses AI‑driven models and domain‑specific logic to decide:

• What is this object?
• How sensitive is it?
• Which regulations or policies apply?
• Who or what can currently access it?

From there, DSPM applies consistent labels to that data, often using frameworks like Microsoft Purview Information Protection (MPIP) so labels are understood by other tools. Those labels are then pushed into your DLP stack, SSE/CASB, and email and endpoint controls, so every enforcement point is working from the same definition of sensitivity, instead of guessing on the fly.

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Once DLP is enforcing on clear labels and context, rather than raw patterns, you no longer need dozens of almost‑duplicate rules per channel. Policies become simpler and more precise, which is what allows teams to realistically drive false positives down by up to half or more.

A Practical Approach to Cutting DLP Noise

If your security team is exhausted by DLP alerts today, you don’t need another round of regex tuning. You need a change in operating model. A pragmatic sequence looks like this.

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Start by measuring the problem instead of just reacting to it. Capture how many DLP alerts you see per week, how many of those are ultimately dismissed, and how much analyst time they consume. Pay special attention to the policies and channels that generate the most noise, because that’s where you’ll see the biggest benefit from a DSPM‑driven approach.

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Next, work with DSPM to turn your noisiest rules into label‑driven policies. Instead of “block any message that looks like it contains a card number,” express the rule as “block files labeled PCI sent to personal domains” or “quarantine emails carrying PHI labels to unapproved partners.” Once Sentra or another DSPM platform is reliably applying those labels, DLP simply has to enforce on them.

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Then, add business context. The same file can be benign in one context and dangerous in another. Combine labels with identity, role, channel, and basic behavior signals like, time of day, destination, volume, etc., so that only genuinely suspicious events result in hard blocks or escalations. A finance export labeled ‘Confidential’ going to an approved auditor should not be treated the same as that export leaving for an unknown Gmail account at midnight.

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Finally, create a feedback loop. Allow analysts to flag alerts as false positives or misconfigurations, and give users controlled ways to override with justification in edge cases. Feed that information back into DSPM tuning and DLP policies at a regular cadence, so your classification and rules get closer to how the business actually operates.

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Over time, you’ll find that you write fewer DLP rules, not more. The rules you do have are easier to explain to stakeholders. And most importantly, your analysts spend their time on true positives and meaningful insider‑risk investigations, not on the hundredth low‑value alert of the week.

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At that point, you haven’t just made DLP tolerable. You’ve turned it into a quiet, reliable enforcement layer sitting on top of a data‑intelligence foundation.

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Sensitive data discovery has become the front door to everything that matters in data security: AI readiness, Microsoft 365 Copilot governance, continuous compliance, and whether your DLP actually works. The days of simply scanning a few databases before an audit are over. Your riskiest information now lives in cloud warehouses, SaaS apps, PDFs, call recordings, and AI pipelines; and most security teams are trying to keep up with tools that were built for a different era.

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If you’re evaluating the best sensitive data discovery tools today, you’ll almost certainly encounter Sentra, BigID, Varonis, and Cyera. All four have credibility in the market. Though they are not interchangeable, especially if you care about AI data security, multi‑cloud DSPM, and keeping data inside your own environment.

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Below is a comparison that reflects what each platform delivers in 2026, followed by a deeper look at where each one fits and why Sentra is increasingly the default choice for AI‑scale, cloud‑first enterprises.

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Side‑by‑Side: Sentra vs BigID vs Varonis vs Cyera

The chart below focuses on the dimensions security and data leaders ask about most often: architecture, coverage, classification quality, AI support, real‑time controls, scale, and fit.

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How to Read This Chart (Without the Hype)

All four of these tools can legitimately call themselves sensitive data discovery platforms:

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• Sentra is built as a cloud‑native DSPM + DAG + DDR platform that keeps data in your environment, with strong AI data readiness and copilot coverage.
• BigID is often chosen for privacy, DSAR, and broad connector needs, especially in Microsoft‑heavy environments.
• Varonis remains a heavyweight for on‑prem file servers and unstructured data with deep permission analytics.
• Cyera focuses on cloud‑native DSPM with agentless posture scanning and some AI‑driven validation.

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Where they diverge is in how far they go beyond “finding data”:

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• Some stop at discovery and classification, leaving access, AI governance, and response to other tools.
• Others focus on specific environments (for example, on‑prem files or S3‑only) and leave gaps in SaaS, AI pipelines, or PDFs, audio, and video.
• Only a Sentra offers in‑place, multi‑cloud coverage with continuous DSPM, DAG, and DDR at truly large scale.

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That’s the lens where Sentra consistently looks strongest, especially if you’re already piloting or rolling out M365 Copilot and other GenAI assistants or have petabytes of regulated data across multi-cloud and hybrid infrastructure.

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Why Sentra Is the Best Fit for AI‑Scale, Multi‑Cloud Discovery

Senra emerges as a clear leader because tt is designed for organizations that:

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• Run at petabyte scale across AWS, Azure, GCP, SaaS, and on‑prem.
• Are under regulatory pressure to show continuous control over PII, PHI, PCI, and IP.
• Are rolling out GenAI and AI copilots but can’t afford accidental data exposure.

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A few traits make Sentra stand out:

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Everything is in‑place and agentless.
Discovery and classification run inside your cloud accounts and data centers using APIs and serverless scanners. Sensitive data isn’t copied into a vendor environment for processing, and scanning doesn’t depend on a forest of agents. That’s both a security benefit and a deployment advantage.

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Sentra understands the data and the business around it.
Sentra’s AI classifier doesn’t stop at matching patterns. It delivers >98% accuracy across structured and unstructured data, and it attaches rich business context: which department owns the data, where it resides geographically, whether it’s synthetic or real, and what role it plays in the business. That context directly drives risk scoring, prioritization, and automated remediation.

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Sentra treats audio, video, and PDFs as first‑class data sources.
Sentra scans dozens of audio and video formats by extracting and transcribing audio with ML models, then running the same classifiers used for text. It also parses complex PDFs, runs OCR on scanned pages, and inspects metadata - all inside your cloud. That closes some of the biggest blind spots in legacy DLP and discovery tools.

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Sentra scales to petabytes without breaking the bank.
Internal and customer bake‑offs show Sentra scanning 9 PB in under 72 hours, with the architecture designed to cover hundreds of petabytes in days and deliver around 10x lower scan cost than older approaches. That makes continuous discovery and re‑scanning feasible instead of a once‑a‑year luxury.

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Sentra unifies DSPM, DAG, and DDR.
Instead of scattering posture, access, and detection across separate siloed tools, Sentra ties them together. It shows you where sensitive data is, who or what can access it, how it’s being used, and what needs to happen next - from revoking access to applying labels or opening tickets - in one place.

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So Which “Best Sensitive Data Discovery Tool” Should You Choose?

If you are primarily focused on:

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• Privacy and DSAR workflows with deep governance in a Microsoft‑centric stack, BigID will be on your shortlist.
• On‑prem file security and permissions analytics for legacy environments, Varonis still deserves serious consideration.
• Cloud‑only DSPM posture checks with agentless deployment and LLM‑augmented validation, Cyera may be attractive in narrower, less regulated scenarios.

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But if you need a single, AI‑ready data security platform that:

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• Discovers and classifies sensitive data across multi‑cloud, SaaS, and on‑prem,
• Keeps data inside your environment while doing it,
• Powers DSPM, DAG, DDR, M365 Copilot governance, and DLP from one consistent data‑context layer, and
• Scales to petabytes without turning each scan into a budgeting exercise,

Then Sentra is, in practice, the best‑fit choice among today’s leading sensitive data discovery tools.

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