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Top 6 Azure Security Tools, Features, and Best Practices

November 7, 2022
6
Min Read

Nowadays, it is evident that the rapid growth of cloud computing has changed how organizations operate. Many organizations increasingly rely on the cloud to drive their daily business operations. The cloud is a single place for storing, processing and accessing data; it’s no wonder that people are becoming addicted to its convenience.

However, as the dependence on cloud service providers continues, the need for security also increases. One needs to measure and safeguard sensitive data to protect against possible threats. Remember that security is a shared responsibility - even if your cloud provider secures your data, the security will not be absolute. Thus, understanding the security features of a particular cloud service provider becomes significant.

Introduction to Microsoft Azure Security Services

Image of Microsoft Azure, explaining how to strengthen security posture with Azure

Microsoft Azure offers services and tools for businesses to manage their applications and infrastructure. Utilizing Azure ensures robust security measures are in place to protect sensitive data, maintain privacy, and mitigate potential threats.

This article will tackle Azure’s security features and tools to help organizations and individuals safeguard and protect their data while they continue their innovation and growth. 

There’s a collective set of security features, services, tools, and best practices offered by Microsoft to protect cloud resources. In this section, let's explore some layers to gain some insights.

The Layers of Security in Microsoft Azure:

Layers of Security Description
Physical Security Microsoft Azure has a strong foundation of physical security measures, and it operates state-of-the-art data centers worldwide with strict physical access controls, which ensures that Azure's infrastructure protects itself against unauthorized physical access.
Network Security Virtual networks, network security groups (NSGs), and distributed denial of service (DDoS) protection create isolated and secure network environments. Microsoft Azure network security mechanisms secure data in transit and protect against unauthorized network access. Of course, we must recognize Azure Virtual Network Gateway, which secures connections between on-premises networks and Azure resources.
Identity and Access Management (IAM) Microsoft Azure offers identity and access management capabilities to control and secure access to cloud resources. The Azure Active Directory (AD) is a centralized identity management platform that allows organizations to manage user identities, enforce robust authentication methods, and implement fine-grained access controls through role-based access control (RBAC).
Data Security Microsoft Azure offers Azure Storage Service Encryption (SSE) which encrypts data at rest, while Azure Disk Encryption secures virtual machine disks. Azure Key Vault provides a secure and centralized location for managing cryptographic keys and secrets.
Threat Detection and Monitoring Microsoft Azure offers Azure Security Center, which provides a centralized view of security recommendations, threat intelligence, and real-time security alerts. Azure Sentinel offers cloud-native security information that helps us quickly detect, alert, investigate, and resolve security incidents.
Compliance and Governance Microsoft Azure offers Azure Policy to define and enforce compliance controls across Azure resources within the organization. Moreover, it helps provide compliance certifications and adhere to industry-standard security frameworks.

Let’s explore some features and tools, and discuss their key features and best practices.

Azure Active Directory Identity Protection

Image of Azure’s Identity Protection page, explaining what is identity protection

Identity protection is a cloud-based service for the Azure AD suite. It focuses on helping organizations protect their user identities and detect potential security risks. Moreover, it uses advanced machine learning algorithms and security signals from various sources to provide proactive and adaptive security measures. Furthermore, leveraging machine learning and data analytics can identify risky sign-ins, compromised credentials, and malicious or suspicious user behavior. How’s that? Sounds great, right?

Key Features

1. Risk-Based User Sign-In Policies

It allows organizations to define risk-based policies for user sign-ins which evaluate user behavior, sign-in patterns, and device information to assess the risk level associated with each sign-in attempt. Using the risk assessment, organizations can enforce additional security measures, such as requiring multi-factor authentication (MFA), blocking sign-ins, or prompting password resets.

2. Risky User Detection and Remediation

The service detects and alerts organizations about potentially compromised or risky user accounts. It analyzes various signals, such as leaked credentials or suspicious sign-in activities, to identify anomalies and indicators of compromise. Administrators can receive real-time alerts and take immediate action, such as resetting passwords or blocking access, to mitigate the risk and protect user accounts.

Best Practices

  • Educate Users About Identity Protection - Educating users is crucial for maintaining a secure environment. Most large organizations now provide security training to increase the awareness of users. Training and awareness help users protect their identities, recognize phishing attempts, and follow security best practices.
  • Regularly Review and Refine Policies - Regularly assessing policies helps ensure their effectiveness, which is why it is good to continuously improve the organization’s Azure AD Identity Protection policies based on the changing threat landscape and your organization's evolving security requirements.

Azure Firewall

Image of Azure Firewall page, explaining what is Azure Firewall

Microsoft offers an Azure Firewall, which is a cloud-based network security service. It acts as a barrier between your Azure virtual networks and the internet. Moreover, it provides centralized network security and protection against unauthorized access and threats. Furthermore, it operates at the network and application layers, allowing you to define and enforce granular access control policies.

Thus, it enables organizations to control inbound and outbound traffic for virtual and on-premises networks connected through Azure VPN or ExpressRoute. Of course, we can’t ignore the filtering traffic of source and destination IP addresses, ports, protocols, and even fully qualified domain names (FQDNs).

Key Features

1. Network and Application-Level Filtering

This feature allows organizations to define rules based on IP addresses (source and destination), including ports, protocols, and FQDNs. Moreover, it helps organizations filter network and application-level traffic, controlling inbound and outbound connections.

2. Fully Stateful Firewall

Azure Firewall is a stateful firewall, which means it can intelligently allow return traffic for established connections without requiring additional rules. The beneficial aspect of this is it simplifies rule management and ensures that legitimate traffic flows smoothly.

3. High Availability and Scalability

Azure Firewall is highly available and scalable. It can automatically scale with your network traffic demand increases and provides built-in availability through multiple availability zones.

Best Practices

  • Design an Appropriate Network Architecture - Plan your virtual network architecture carefully to ensure proper placement of Azure Firewall. Consider network segmentation, subnet placement, and routing requirements to enforce security policies and control traffic flow effectively.
  • Implement Network Traffic Filtering Rules - Define granular network traffic filtering rules based on your specific security requirements. Start with a default-deny approach and allow only necessary traffic. Regularly review and update firewall rules to maintain an up-to-date and effective security posture.
  • Use Application Rules for Fine-Grain Control - Leverage Azure Firewall's application rules to allow or deny traffic based on specific application protocols or ports. By doing this, organizations can enforce granular access control to applications within their network.

Azure Resource Locks

Image of Azure Resource Locks page, explaining how to lock your resources to protect your infrastructure

Azure Resource Locks is a Microsoft Azure feature that allows you to restrict Azure resources to prevent accidental deletion or modification. It provides an additional layer of control and governance over your Azure resources, helping mitigate the risk of critical changes or deletions.

Key Features

Two types of locks can be applied:

1. Read-Only (CanNotDelete)

This lock type allows you to mark a resource as read-only, meaning modifications or deletions are prohibited.

2. CanNotDelete (Delete)

This lock type provides the highest level of protection by preventing both modifications and deletions of a resource; it ensures that the resource remains completely unaltered.

Best Practices

  • Establish a Clear Governance Policy - Develop a governance policy that outlines the use of Resource Locks within your organization. The policy should define who has the authority to apply or remove locks and when to use locks, and any exceptions or special considerations.
  • Leverage Azure Policy for Lock Enforcement - Use Azure Policy alongside Resource Locks to enforce compliance with your governance policies. It is because Azure Policy can automatically apply locks to resources based on predefined rules, reducing the risk of misconfigurations.

Azure Secure SQL Database Always Encrypted

Image of Azure Always Encrypted page, explaining how it works

Azure Secure SQL Database Always Encrypted is a feature of Microsoft Azure SQL Database that provides another security-specific layer for sensitive data. Moreover, it protects data at rest and in transit, ensuring that even database administrators or other privileged users cannot access the plaintext values of the encrypted data.

Key Features

1. Client-Side Encryption

Always Encrypted enables client applications to encrypt sensitive data before sending it to the database. As a result, the data remains encrypted throughout its lifecycle and can be decrypted only by an authorized client application.

2. Column-Level Encryption

Always Encrypted allows you to selectively encrypt individual columns in a database table rather than encrypting the entire database. It gives organizations fine-grained control over which data needs encryption, allowing you to balance security and performance requirements.

3. Transparent Data Encryption

The database server stores the encrypted data using a unique encryption format, ensuring the data remains protected even if the database is compromised. The server is unaware of the data values and cannot decrypt them.

Best Practices

The organization needs to plan and manage encryption keys carefully. This is because encryption keys are at the heart of Always Encrypted. Consider the following best practices.

  • Use a Secure and Centralized Key Management System - Store encryption keys in a safe and centralized location, separate from the database. Azure Key Vault is a recommended option for managing keys securely.
  • Implement Key Rotation and Backup - Regularly rotate encryption keys to mitigate the risks of key compromise. Moreover, establish a key backup strategy to recover encrypted data due to a lost or inaccessible key.
  • Control Access to Encryption Keys - Ensure that only authorized individuals or applications have access to the encryption keys. Applying the principle of least privilege and robust access control will prevent unauthorized access to keys.

Azure Key Vault

Image of Azure Key Vault page

Azure Key Vault is a cloud service provided by Microsoft Azure that helps safeguard cryptographic keys, secrets, and sensitive information. It is a centralized storage and management system for keys, certificates, passwords, connection strings, and other confidential information required by applications and services. It allows developers and administrators to securely store and tightly control access to their application secrets without exposing them directly in their code or configuration files.

Key Features

1. Key Management

Key Vault provides a secure key management system that allows you to create, import, and manage cryptographic keys for encryption, decryption, signing, and verification.

2. Secret Management

It enables you to securely store (as plain text or encrypted value) and manage secrets such as passwords, API keys, connection strings, and other sensitive information.

3. Certificate Management

Key Vault supports the storage and management of X.509 certificates, allowing you to securely store, manage, and retrieve credentials for application use.

4. Access Control

Key Vault provides fine-grained access control to manage who can perform operations on stored keys and secrets. It integrates with Azure Active Directory (Azure AD) for authentication and authorization.

Best Practices

  • Centralized Secrets Management - Consolidate all your application secrets and sensitive information in Key Vault rather than scattering them across different systems or configurations. The benefit of this is it simplifies management and reduces the risk of accidental exposure.
  • Use RBAC and Access Policies - Implement role-based access control (RBAC) and define granular access policies to power who can perform operations on Key Vault resources. Follow the principle of least privilege, granting only the necessary permissions to users or applications.
  • Secure Key Vault Access - Restrict access to Key Vault resources to trusted networks or virtual networks using virtual network service or private endpoints because it helps prevent unauthorized access to the internet.

Azure AD Multi-Factor Authentication

Image of Azure AD Multi-Factor Authentication page, explaining how it works

It is a security feature provided by Microsoft Azure that adds an extra layer of protection to user sign-ins and helps safeguard against unauthorized access to resources. Users must give additional authentication factors beyond just a username and password.

Key Features

1. Multiple Authentication Methods

Azure AD MFA supports a range of authentication methods, including phone calls, text messages (SMS), mobile app notifications, mobile app verification codes, email, and third-party authentication apps. This flexibility allows organizations to choose the methods that best suit their users' needs and security requirements.

2. Conditional Access Policies

Azure AD MFA can configure conditional access policies, allowing organizations to define specific conditions under which MFA (is required), once applied to an organization, on the user location, device trust, application sensitivity, and risk level. This granular control helps organizations strike a balance between security and user convenience.

Best Practices

  • Enable MFA for All Users - Implement a company-wide policy to enforce MFA for all users, regardless of their roles or privileges, because it will ensure consistent and comprehensive security across the organization.
  • Use Risk-Based Policies - Leverage Azure AD Identity Protection and its risk-based policies to dynamically adjust the level of authentication required based on the perceived risk of each sign-in attempt because it will help balance security and user experience by applying MFA only when necessary.
  • Implement Multi-Factor Authentication for Privileged Accounts - Ensure that all privileged accounts, such as administrators and IT staff, are protected with MFA. These accounts have elevated access rights and are prime targets for attackers. Enforcing MFA adds an extra layer of protection to prevent unauthorized access.

Conclusion

In this post, we have introduced the importance of cybersecurity in the cloud space due to dependence on cloud providers. After that we discussed some layers of security in Azure to gain insights about its landscape and see some tools and features available. Of course we can’t ignore the features such as Azure Active Directory Identity Protection, Azure Firewall, Azure Resource Locks, Azure Secure SQL Database Always Encrypted, Azure Key Vault and Azure AD Multi-Factor Authentication by giving an overview on each, its key features and the best practices we can apply to our organization.

Ready to go beyond native Azure tools?

While Azure provides powerful built-in security features, securing sensitive data across multi-cloud environments requires deeper visibility and control.

Request a demo with Sentra to see how our platform complements Azure by discovering, classifying, and protecting sensitive data - automatically and continuously.

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Discover Ron’s expertise, shaped by over 20 years of hands-on tech and leadership experience in cybersecurity, cloud, big data, and machine learning. As a serial entrepreneur and seed investor, Ron has contributed to the success of several startups, including Axonius, Firefly, Guardio, Talon Cyber Security, and Lightricks, after founding a company acquired by Oracle.

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Ariel Rimon
Ariel Rimon
March 30, 2026
3
Min Read

Web Archive Scanning: WARC, ARC, and the Forgotten PII in Your Compliance Crawls

Web Archive Scanning: WARC, ARC, and the Forgotten PII in Your Compliance Crawls

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.

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.

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.

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:

  • 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:

  • 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.

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.

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.

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.

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:

  • 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:

  • 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:

  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:

  • 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:

  • 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.

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.

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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Nikki Ralston
Nikki Ralston
March 29, 2026
3
Min Read

DLP False Positives Are Drowning Your Security Team: How to Cut Noise with DSPM

DLP False Positives Are Drowning Your Security Team: How to Cut Noise with DSPM

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.

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.

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.

In practice, three things tend to go wrong:

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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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Ward Balcerzak
Ward Balcerzak
March 26, 2026
3
Min Read

Best Sensitive Data Discovery Tools in 2026

Best Sensitive Data Discovery Tools in 2026

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.

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.

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.

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.

Capability Sentra BigID Varonis Cyera
Architecture & where data lives Cloud-native, agentless platform that scans data in-place across clouds, SaaS, and on-prem. Data never leaves the customer environment; only metadata and findings are processed. Cloud-centric discovery platform with SaaS control plane. Often relies on connectors and moving metadata or samples into its environment for analysis. Built around on-prem collectors and agents. Deploys locally but sends metadata to its platform for analytics. Cloud-native DSPM with agentless approach, but often requires data or metadata to leave the environment for analysis.
Coverage Broadest coverage across IaaS, PaaS, SaaS, and on-prem, including structured and unstructured data. Very broad connectors across SaaS and data platforms, but depends on configuration. Strong for unstructured and on-prem; cloud and SaaS coverage improving. Good cloud/SaaS coverage but weaker on-prem and structured depth.
Classification quality AI/ML-enhanced with >98% accuracy and deep business context (ownership, sensitivity, purpose). Strong classification but higher false negatives in complex scenarios. Rich classifiers but complex tuning and heavier rescans. Less contextual, higher false positives, more validation required.
AI & Copilot security Purpose-built for AI risks: Copilot readiness, agent inventory, data access mapping, identity-based guardrails. Strong governance via Purview but less unified AI security view. Emerging AI use cases, not core focus. LLM-based validation but limited visibility into AI data movement.
DSPM + DAG + DDR Unified platform combining posture, access governance, and detection/response in real time. Strong discovery and privacy workflows; relies on integrations for detection. Very strong DAG for permissions, limited DDR for cloud threats. DSPM-focused; no native DDR and limited real-time threat linkage.
Time to value Fast agentless deployment; insights day one, full coverage in days. Heavier setup with connectors and integrations. Long deployment cycles due to agents and integrations. Quick start but slower full inventory at scale.
Scale & cost Petabyte-scale efficiency; scans tens of PB in days with very low cost. Predictable pricing but higher compute cost at scale. Higher operational cost at large scale. Scales but with higher resource consumption and cost.
Best fit Large cloud-first enterprises needing unified DSPM, DAG, DDR and AI governance. Organizations prioritizing privacy workflows and Microsoft ecosystem. Enterprises focused on on-prem file security and permissions. Cloud-native DSPM use cases with narrower scope.

How to Read This Chart (Without the Hype)

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

  • 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.

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

  • 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.

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.

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:

A few traits make Sentra stand out:

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.

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.

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.

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.

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.

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

If you are primarily focused on:

  • 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.

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

  • 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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