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Top 6 Azure Security Tools, Features, and Best Practices

November 7, 2022

Min Read

Ron Reiter

Co-Founder and CTO

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.

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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

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

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

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

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.

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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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Ron Reiter

Co-Founder and CTO

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.

Latest Blog Posts

Ofir Yehoshua

November 17, 2025

Min Read

How to Gain Visibility and Control in Petabyte-Scale Data Scanning

Every organization today is drowning in data - millions of assets spread across cloud platforms, on-premises systems, and an ever-expanding landscape of SaaS tools. Each asset carries value, but also risk. For security and compliance teams, the mandate is clear: sensitive data must be inventoried, managed and protected.

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Scanning every asset for security and compliance is no longer optional, it’s the line between trust and exposure, between resilience and chaos.

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Many data security tools promise to scan and classify sensitive information across environments. In practice, doing this effectively and at scale, demands more than raw ‘brute force’ scanning power. It requires robust visibility and management capabilities: a cockpit view that lets teams monitor coverage, prioritize intelligently, and strike the right balance between scan speed, cost, and accuracy.

Why Scan Tracking Is Crucial

Scanning is not instantaneous. Depending on the size and complexity of your environment, it can take days - sometimes even weeks to complete. Meanwhile, new data is constantly being created or modified, adding to the challenge.

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Without clear visibility into the scanning process, organizations face several critical obstacles:

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Unclear progress: It’s often difficult to know what has already been scanned, what is currently in progress, and what remains pending. This lack of clarity creates blind spots that undermine confidence in coverage.
Time estimation gaps: In large environments, it’s hard to know how long scans will take because so many factors come into play — the number of assets, their size, the type of data - structured, semi-structured, or unstructured, and how much scanner capacity is available. As a result, predicting when you’ll reach full coverage is tricky. This becomes especially stressful when scans need to be completed before a fixed deadline, like a compliance audit.

"With Sentra’s Scan Dashboard, we were able to quickly scale up our scanners to meet a tight audit deadline, finish on time, and then scale back down to save costs. The visibility and control it gave us made the whole process seamless”, said CISO of Large Retailer.‍
Poor prioritization: Not all environments or assets carry the same importance. Yet without visibility into scan status, teams struggle to balance historical scans of existing assets with the ongoing influx of newly created data, making it nearly impossible to prioritize effectively based on risk or business value.

Sentra’s End-to-End Scanning Workflow

Managing scans at petabyte scale is complex. Sentra streamlines the process with a workflow built for scale, clarity, and control that features:

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1. Comprehensive Asset Discovery

Before scanning even begins, Sentra automatically discovers assets across cloud platforms, on-premises systems, and SaaS applications. This ensures teams have a complete, up-to-date inventory and visual map of their data landscape, so no environment or data store is overlooked.

Example: New S3 buckets, a freshly deployed BigQuery dataset, or a newly connected SharePoint site are automatically identified and added to the inventory.
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Comprehensive Asset Discovery with Sentra

2. Configurable Scan Management

Administrators can fine-tune how scans are executed to meet their organization’s needs. With flexible configuration options, such as number of scanners, sampling rates, and prioritization rules - teams can strike the right balance between scan speed, coverage, and cost control.

For instance, compliance-critical assets can be scanned at full depth immediately, while less critical environments can run at reduced sampling to save on compute consumption and costs.

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3. Real-Time Scan Dashboard

Sentra’s unified Scan Dashboard provides a cockpit view into scanning operations, so teams always know where they stand. Key features include:

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Daily scan throughput correlated with the number of active scanners, helping teams understand efficiency and predict completion times.
Coverage tracking that visualizes overall progress and highlights which assets remain unscanned.
Decision-making tools that allow teams to dynamically adjust, whether by adding scanner capacity, changing sampling rates, or reordering priorities when new high-risk assets appear.

Handling Data Changes

The challenge doesn’t end once the initial scans are complete. Data is dynamic, new files are added daily, existing records are updated, and sensitive information shifts locations. Sentra’s activity feeds give teams the visibility they need to understand how their data landscape is evolving and adapt their data security strategies in real time.

Conclusion

Tracking scan status at scale is complex but critical to any data security strategy. Sentra provides an end-to-end view and unmatched scan control, helping organizations move from uncertainty to confidence with clear prediction of scan timelines, faster troubleshooting, audit-ready compliance, and smarter, cost-efficient decisions for securing data.

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Ward Balcerzak

November 12, 2025

Min Read

Data Security

Best DSPM Tools: Top 9 Vendors Compared

Enhanced DSPM Adoption Is the Most Important Data Security Trend of 2026

Over the past few years, organizations have realized that traditional security tools can’t keep pace with how data moves and grows today. Exploding volumes of sensitive data now flourish across multi-cloud environments, SaaS platforms, and AI systems, often without full visibility by the teams responsible for securing it. Unstructured data presents the greatest risk - representing over 80% of corporate data.

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That’s why Data Security Posture Management (DSPM) has become a critical part of the modern security stack. DSPM tools help organizations automatically discover, classify, monitor, and protect sensitive data - no matter where it lives or travels.

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But in 2026, the data security game is changing. Many DSPMs can tell you what your data is, but more is needed. Leading DSPM platforms are going beyond visibility. They’re delivering real-time AI-enhanced contextual business insights, automated remediation, and AI-aware accurate protection that scales with your dynamic data.

AI-enhanced DSPM Capabilities in 2026

Not all DSPM tools are built the same. The top platforms share a few key traits that define the next generation of data security posture management:

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Capability	Why It Matters
Continuous discovery and classification at scale	Real-time visibility into all sensitive data across cloud, SaaS, and on-prem systems. Efficiency, at petabyte scale, to allow for scanning frequency commensurate with business risk.
Contextual risk analysis	Understanding what data is sensitive, who can access it, and how it’s being used. Understanding the business context around data so that appropriate actions can be taken.
Automated remediation	Native capabilities and Integration with systems that correct risky configurations or excessive access automatically.
Integration and scalability	Seamless connections to CSPM, SIEM, IAM, ITSM, and SOAR tools to unify data risk management and streamline workflows.
AI and model governance	Capabilities to secure data used in GenAI agents, copilot assistants, and pipelines.

Top DSPM Tools to Watch in 2026

Based on recent analyst coverage, market growth, and innovation across the industry, here are the top DSPM platforms to watch this year, each contributing to how data security is evolving.

1. Sentra

As a cloud-native DSPM platform, Sentra focuses on continuous data protection, not just visibility. It discovers and accurately classifies sensitive data in real time across all cloud environments, while automatically remediating risks through policy-driven automation.

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What sets Sentra apart:

Continuous, automated discovery and classification across your entire data estate - cloud, SaaS, and on-premises.
Business Contextual insights that understand the purpose of data, accurately linking data, identity, and risk.
Automatic learning to discern customer unique data types and continuously improve labeling over time.
Petabyte scaling and low compute consumption for 10X cost efficiency.
Automated remediation workflows and integrations to fix issues instantly.
Built-in coverage for data flowing through AI and SaaS ecosystems.

Ideal for: Security teams looking for a cloud-native DSPM platform built for scalability in the AI era with automation at its core.

2. BigID

A pioneer in data discovery and classification, BigID bridges DSPM and privacy governance, making it a good choice for compliance-heavy sectors.

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Ideal for: Organizations prioritizing data privacy, governance, and audit readiness.

3. Prisma Cloud (Palo Alto Networks)

Prisma’s DSPM offering integrates closely with CSPM and CNAPP components, giving security teams a single pane of glass for infrastructure and data risk.

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Ideal for: Enterprises with hybrid or multi-cloud infrastructures already using Palo Alto tools.

4. Microsoft Purview / Defender DSPM

Microsoft continues to invest heavily in DSPM through Purview, offering rich integration with Microsoft 365 and Azure ecosystems. Note: Sentra integrates with Microsoft Purview Information Protection (MPIP) labeling and DLP policies.

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Ideal for: Microsoft-centric organizations seeking native data visibility and compliance automation.

5. Securiti.ai

Positioned as a “Data Command Center,” Securiti unifies DSPM, privacy, and governance. Its strength lies in automation and compliance visibility and SaaS coverage.

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Ideal for: Enterprises looking for an all-in-one governance and DSPM solution.

6. Cyera

Cyera has gained attention for serving the SMB segment with its DSPM approach. It uses LLMs for data context, supplementing other classification methods, and provides integrations to IAM and other workflow tools.

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Ideal for: Small/medium growing companies that need basic DSPM functionality.

7. Wiz

Wiz continues to lead in cloud security, having added DSPM capabilities into its CNAPP platform. They’re known for deep multi-cloud visibility and infrastructure misconfiguration detection.

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Ideal for: Enterprises running complex cloud environments looking for infrastructure vulnerability and misconfiguration management.

8. Varonis

Varonis remains a strong player for hybrid and on-prem data security, with deep expertise in permissions and access analytics and focus on SaaS/unstructured data.

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Ideal for: Enterprises with legacy file systems or mixed cloud/on-prem architectures.

9. Netwrix

Netwrix’s platform incorporates DSPM-related features into its auditing and access control suite.
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‍Ideal for: Mid-sized organizations seeking DSPM as part of a broader compliance solution.

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Emerging DSPM Trends to Watch in 2026

AI Data Security: As enterprises adopt GenAI, DSPM tools are evolving to secure data used in training and inference.
Identity-Centric Risk: Understanding and controlling both human and machine identities is now central to data posture.
Automation-Driven Security: Remediation workflows are becoming the differentiator between “good” and “great.”

Market Consolidation: Expect to see CNAPP, legacy security, and cloud vendors acquiring DSPM startups to strengthen their coverage.

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How to Choose the Right DSPM Tool

When evaluating a DSPM solution, align your choice with your data landscape and goals:

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Cloud-Native Company Choose tools designed for cloud-first environments (like Sentra, Securiti, Wiz).
Compliance Priority Platforms like Sentra, BigID or Securiti excel in privacy and governance.
Microsoft-Heavy Stack Purview and Sentra DSPM offer native integration.
Hybrid Environment Consider Varonis, Prisma Cloud, or Sentra for extended visibility.
Enterprise Scalability Evaluate deployment ease, petabyte scalability, cloud resource consumption, scanning efficiency, etc. (Sentra excels here)

*Pro Tip: Run a proof of concept (POC) across multiple environments to test scalability, accuracy, and operational cost effectiveness before full deployment.

Final Thoughts: DSPM Is About Action

The best DSPM tools in 2026 share one core principle, they help organizations move from visibility to action.

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At Sentra, we believe that the future of DSPM lies in continuous, automated data protection:

Real-time discovery of sensitive data @ scale
Context-aware prioritization for business insight
Automated remediation that reduces risk instantly

As data continues to power AI, analytics, and innovation, DSPM ensures that innovation never comes at the cost of security. See how Sentra helps leading enterprises protect data across multi-cloud and SaaS environments.

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Gilad Golani

November 6, 2025

Min Read

How SLMs (Small Language Models) Make Sentra’s AI Faster and More Accurate

The LLM Hype, and What’s Missing

Over the past few years, large language models (LLMs) have dominated the AI conversation. From writing essays to generating code, LLMs like GPT-4 and Claude have proven that massive models can produce human-like language and reasoning at scale.

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But here's the catch: not every task needs a 70-billion-parameter model. Parameters are computationally expensive - they require both memory and processing time.

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At Sentra, we discovered early on that the work our customers rely on for accurate, scalable classification of massive data flows - isn’t about writing essays or generating text. It’s about making decisions fast, reliably, and cost-effectively across dynamic, real-world data environments. While large language models (LLMs) are excellent at solving general problems, it creates a lot of unnecessary computational overhead.

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That’s why we’ve shifted our focus toward Small Language Models (SLMs) - compact, specialized models purpose-built for a single task - understanding and classifying data efficiently. By running hundreds of SLMs in parallel on regular CPUs, Sentra can deliver faster insights, stronger data privacy, and a dramatically lower total cost of AI-based classification that scales with their business, not their cloud bill.

What Is an SLM?

An SLM is a smaller, domain-specific version of a language model. Instead of trying to understand and generate any kind of text, an SLM is trained to excel at a particular task, such as identifying the topic of a document (what the document is about or what type of document it is), or detecting sensitive entities within documents, such as passwords, social security numbers, or other forms of PII.

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In other words: If an LLM is a generalist, an SLM is a specialist. At Sentra, we use SLMs that are tuned and optimized for security data classification, allowing them to process high volumes of content with remarkable speed, consistency, and precision. These SLMs are based on standard open source models, but trained with data that was curated by Sentra, to achieve the level of accuracy that only Sentra can guarantee.

From LLMs to SLMs: A Strategic Evolution

Like many in the industry, we started by testing LLMs to see how well they could classify and label data. They were powerful, but also slow, expensive, and difficult to scale. Over time, it became clear: LLMs are too big and too expensive to run on customer data for Sentra to be a viable, cost effective solution for data classification.

Each SLM handles a focused part of the process: initial categorization, text extraction from documents and images, and sensitive entity classification. The SLMs are not only accurate (even more accurate than LLMs classifying using prompts) - they can run on standard CPUs efficiently, and they run inside the customer’s environment, as part of Sentra’s scanners.

The Benefits of SLMs for Customers

a. Speed and Efficiency

SLMs process data faster because they’re lean by design. They don’t waste cycles generating full sentences or reasoning across irrelevant contexts. This means real-time or near-real-time classification, even across millions of data points.

b. Accuracy and Adaptability

SLMs are pre-trained “zero-shot” language models that can categorize and classify generically, without the need to pre-train on a specific task in advance. This is the meaning of “zero shot” - it means that regardless of the data it was trained on, the model can classify an arbitrary set of entities and document labels without training on each one specifically. This is possible due to the fact that language models are very advanced, and they are able to capture deep natural language understanding at the training stage.

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Regardless of that, Sentra fine tunes these models to further increase the accuracy of the classification, by curating a very large set of tagged data that resembles the type of data that our customers usually run into.

Our feedback loops ensure that model performance only gets better over time - a direct reflection of our customers’ evolving environments.

c. Cost and Sustainability

Because SLMs are compact, they require less compute power, which means lower operational costs and a smaller carbon footprint. This efficiency allows us to deliver powerful AI capabilities to customers without passing on the heavy infrastructure costs of running massive models.

d. Security and Control

Unlike LLMs hosted on external APIs, SLMs can be run within Sentra’s secure environment, preserving data privacy and regulatory compliance. Customers maintain full control over their sensitive information - a critical requirement in enterprise data security.

A Quick Comparison: SLMs vs. LLMs

The difference between SLMs and LLMs becomes clear when you look at their performance across key dimensions:

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Factor	SLMs	LLMs
Speed	Fast, optimized for classification throughput	Slower and more compute-intensive for large-scale inference
Cost	Cost-efficient	Expensive to run at scale
Accuracy (for simple tasks)	Optimized for classification	Comparable but unnecessary overhead
Deployment	Lightweight, easy to integrate	Complex and resource-heavy
Adaptability (with feedback)	Continuously fine-tuned, ability to fine tune per customer	Harder to customize, fine-tuning costly
Best Use Case	Classification, tagging, filtering	Reasoning and analysis, generation, synthesis

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Continuous Learning: How Sentra’s SLMs Grow

One of the most powerful aspects of our SLM approach is continuous learning. Each Sentra customer project contributes valuable insights, from new data patterns to evolving classification needs. These learnings feed back into our training workflows, helping us refine and expand our models over time.

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While not every model retrains automatically, the system is built to support iterative optimization: as our team analyzes feedback and performance, models can be fine-tuned or extended to handle new categories and contexts.

The result is an adaptive ecosystem of SLMs that becomes more effective as our customer base and data diversity grow, ensuring Sentra’s AI remains aligned with real-world use cases.

Sentra’s Multi-SLM Architecture

Sentra’s scanning technology doesn’t rely on a single model. We run many SLMs in parallel, each specializing in a distinct layer of classification:

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Embedding models that convert data into meaningful vector representations
Entity Classification models that label sensitive entities
Document Classification models that label documents by type
Image-to-text and speech-to-text models that are able to process non-textual data into textual data

This layered approach allows us to operate at scale - quickly, cheaply, and with great results. In practice, that means faster insights, fewer errors, and a more responsive platform for every customer.

The Future of AI Is Specialized

We believe the next frontier of AI isn’t about who can build the biggest model, it’s about who can build the most efficient, adaptive, and secure ones.

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By embracing SLMs, Sentra is pioneering a future where AI systems are purpose-built, transparent, and sustainable. Our approach aligns with a broader industry shift toward task-optimized intelligence - models that do one thing extremely well and can learn continuously over time.

Conclusion: The Power of Small

At Sentra, we’ve learned that in AI, bigger isn’t always better. Our commitment to SLMs reflects our belief that efficiency, adaptability, and precision matter most for customers. By running thousands of small, smart models rather than a single massive one, we’re able to classify data faster, cheaper, and with greater accuracy - all while ensuring customer privacy and control.

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In short: Sentra’s SLMs represent the power of small, and the future of intelligent classification.

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