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Prevent Sensitive Data Breaches With Data Detection & Response (DDR)

January 21, 2024
4
Min Read
Data Security

Amidst the dynamic cybersecurity landscape, the need for advanced Threat Detection and Incident Response (TDIR) solutions has never been more crucial. Traditional tools often focus on addressing the complexities of security without data awareness. This deficiency can result in signal fatigue, and increased time to investigate.

Data Detection and Response (DDR) distinguishes itself by focusing on data-first threats, such as: compromise or manipulation of sensitive databases, unauthorized disclosure of sensitive information, intellectual property theft, and many other malicious activities targeting sensitive information. Finally, the obligation to inform and potentially compensate affected parties in compliance with regulatory requirements strengthens the need to enrich TDIR with a data-focused technology.

In this blog, we will start by explaining the difference between data detection and response (DDR) and cloud detection and response (CDR), and how data detection and response (DDR) fits into a cloud data security platform. We will then decode the distinctions between DDR and other TDIR solutions like Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR). Lastly, we will explore why Sentra, with its DDR approach, emerges as a comprehensive and efficient data security solution.

Challenges in Traditional Approaches

Classifying data accurately poses a significant challenge to most traditional cybersecurity approaches. Behavioral analysis, while effective, often overlooks the critical aspect of data type, leading to potential blind spots and excessive false positives. Real-time prevention measures also face limitations, such as they can only protect the platforms they have visibility into, often restricting them to known and managed infrastructure, leaving organizations vulnerable to sophisticated cyber threats that target the public cloud.

Differences Between Data Detection and Response (DDR) and Cloud Detection and Response (CDR)

Cloud detection and response (CDR) solutions focus on overseeing and safeguarding cloud infrastructure, while data detection and response (DDR) specialize in the surveillance and protection of data. DDR plays a crucial role in identifying potential threats to sensitive data, irrespective of its location or format, providing an essential layer of security that goes beyond the capabilities of solutions focusing solely on infrastructure. Additionally, DDR empowers organizations to concentrate on detecting and addressing potential risks to their most sensitive data, reducing noise, cutting costs, and preventing alert fatigue.

When incorporating DDR into a cloud data security platform, organizations should see it as a crucial part of a strategy that encompasses technologies like data security posture management (DSPM), data access governance, and compliance management. This integration enables comprehensive security measures throughout the data lifecycle, enhancing overall cloud data security.

Why do I need a DDR if I’m already using a CDR product?

Data Detection and Response (DDR) is focused on monitoring data access activities that are performed by users and applications, while CDR is focused on infrastructure resources, such as their creation and configuration changes. DDR and CDR serve as detection and response tools, yet they offer distinct sets of threat detection capabilities essential for organizations aiming to prevent cloud data breaches and ransomware attacks.

Some examples where DDR can identify data-centric threats that might go unnoticed by CDR:

  1. Users who download sensitive data types that they don’t usually access.
  2. A ransomware attack in which amounts of business-critical data is being encrypted or deleted.
  3. Users or applications who gain access to sensitive data via a privilege escalation. 
  4. Tampering or poisoning of a Large Language Model (LLM) training dataset by a 3rd party application.
  5. Supply chain attack detection when a compromised third party app is exfiltrating sensitive data from your cloud environment.
  6. Credentials extraction of high-impact keys that have access to sensitive data.

Lastly, DDR offers security operations center (SOC) teams to focus on what matters the most – attacks on their sensitive data, hence reducing the noise and saving time. While CDR detects threats such as impossible travel or brute force log-in attempts on any cloud resources, DDR detects such threats only when the target cloud resources contain sensitive data.

Threat Detection and Incident Response (TDIR) Solutions

Endpoint Detection and Response (EDR)

In the ever-evolving landscape of cybersecurity, Endpoint Detection and Response (EDR) plays a pivotal role in safeguarding the digital perimeters of organizations. Focused on monitoring and responding to suspicious activities at the endpoint level, EDR solutions are crucial for identifying and neutralizing threats before they escalate. Armed with advanced analytics and machine learning algorithms, EDR empowers technical teams to detect anomalous behavior, conduct thorough investigations, and orchestrate rapid responses to potential security incidents.

Extended Detection and Response (XDR)

Extended Detection and Response (XDR) is a solution designed to fortify organizations against sophisticated threats and extend protection beyond EDR. XDR seamlessly integrates threat intelligence, endpoint detection, and incident response across multiple security layers, offering a unified defense strategy. By aggregating and correlating data from various sources such as servers, applications, and other infrastructure, XDR provides unparalleled visibility into potential threats, enabling rapid detection and response. Its proactive approach enhances incident investigation and remediation, ultimately minimizing the impact of cyber threats across an organization's IT estate.

Enter DDR: Revolutionizing Data Security

Data Detection and Response (DDR) brings real-time threat detection to complement data posture controls, hence combining with Data Security Posture Management (DSPM) to address these longstanding challenges. Sentra, a leading player in this domain, ensures real-time data protection across various cloud environments, offering a comprehensive solution to safeguard data wherever it resides. DDR provides a layer of real-time threat detection that is agnostic to infrastructure and works well in multi-cloud environments - it works no matter where data travels.

DDR provides rich near real-time context to complement DSPM. Sentra’s DDR is not dependent on scanning your data. Instead, it continually monitors log activity (ex. AWS CloudTrail events) and can alert on any suspicious or unusual activity such as an exfiltration or unusual access - this can be from a malicious insider or outsider or simply unintended actions from an authorized user or a supply chain partner. Combined with DSPM, DDR provides enhanced context regarding data usage and related exposure. Sentra can help an organization to focus monitoring efforts on areas of greatest risk and reduce the ‘noise’ (false positives or inactionable alarms) from less contextually aware activity monitors.

Proactive and Reactive Data Security with Sentra's DSPM and DDR

Sentra takes a dual-pronged approach, combining proactive and reactive controls to fortify data security at every stage of a potential cyberattack:

  • Weakening Defenses Detection: Continuously monitor for unauthorized changes to data security posture, identifying escalated access privileges or changes in encryption levels.
  • Suspicious Access Detection: Instant alerts are triggered when a third party or insider accesses sensitive information, enabling swift action to prevent potential malicious activities.
  • Reconnaissance: Detect an early stage of the attack when an attacker moves sensitive data across and within cloud networks in order to prepare for the data exfiltration stage.
  • Data Loss and Ransomware Prevention: Real-time monitoring and alerts for accidental or unauthorized data movement, coupled with the enforcement of least privilege data access, prevent potential breaches.
  • Data Exfiltration Detection: Sentra detects anomalous sensitive data movement in near real-time, providing quick notification and remediation before significant damages occur.
  • Breach Recovery Acceleration: In the unfortunate event of a breach, Sentra provides guidance and contextual information, streamlining post-incident analysis and remediation.

Seamless Integration for Enhanced Efficiency

Sentra provides seamless integration into your security workflow. With over 20 pre-built or custom integrations, Sentra ensures that alert context is directly fed to the appropriate teams, expediting issue resolution. This integrated approach enables organizations to respond to potential threats with unmatched speed and efficiency.

Attribute EDR XDR CDR DDR
Monitored environment Endpoints (laptops, desktops, servers, mobile devices) Multiple security layers (endpoints, networks, cloud, email, etc.) Cloud assets and infrastructure Data repositories within the cloud environment
Threat detection method Behavior-based, signature-based, machine learning Correlation of data from multiple sources, machine learning, AI Log analysis, anomaly detection, machine learning Data-aware detection rules and behavioral analysis based on data access
Presence requirement Agent installed on endpoints Integration with multiple security tools Typically agentless, can have agents on cloud resources Typically agentless, Data collection from various sources, not limited to endpoint
Example Vendor CrowdStrike, SentinelOne, Microsoft Defender for Endpoint Trend Micro Vision One, Palo Alto Networks Cortex XDR, Cisco SecureX Wiz, Rapid7 InsightIDR, FireEye Helix Sentra DDR, Exabeam, Securonix, LogRhythm


Data Detection and Response (DDR) is not a replacement or superior solution, it is complementary to the others.

Companies need these technologies for different reasons:

  • EDR for endpoint
  • XDR for on premise
  • CDR for cloud infrastructure
  • DDR for cloud data stores
sensitive data that was accessed from suspicious IP address

With Sentra, organizations get the best of both worlds – proactive and reactive controls integrated for complete data protection. Sentra combines DDR with powerful Data Security Posture Management (DSPM), allowing users to detect and remediate data security risks efficiently. It's time to revolutionize data security with Sentra’s Data Detection and Response (DDR) – your comprehensive solution to safeguarding your most valuable asset: your data.

To learn more, schedule a demo with one of our data security experts.

Alex has nearly a decade of extensive programming experience in the areas of Computer Networks and Cyber Security, with emphasis on Python, Go, C++ programming, software design, research and development of network protocols. He specializes in back-end development, and is currently the Data Engineering Team Lead at Sentra. Read his articles about topics like data detection and response (DDR), accurate data classification, and more.

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Ward Balcerzak
Ward Balcerzak
November 12, 2025
4
Min Read
Data Security

Best DSPM Tools: Top 9 Vendors Compared

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.

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.

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:

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.

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.


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.


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.

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.


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.


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.

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.


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.

Ideal for: Mid-sized organizations seeking DSPM as part of a broader compliance solution.

Emerging DSPM Trends to Watch in 2026

  1. AI Data Security: As enterprises adopt GenAI, DSPM tools are evolving to secure data used in training and inference.

  2. Identity-Centric Risk: Understanding and controlling both human and machine identities is now central to data posture.

  3. 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.

How to Choose the Right DSPM Tool

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

  • 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.

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
Gilad Golani
November 6, 2025
4
Min Read

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

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.

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.

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.

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.

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.

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:

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

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.

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:

  1. Embedding models that convert data into meaningful vector representations
  2. Entity Classification models that label sensitive entities
  3. Document Classification models that label documents by type
  4. 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.

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.

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

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Aarti Gadhia
Aarti Gadhia
October 27, 2025
3
Min Read
Data Security

My Journey to Empower Women in Cybersecurity

My Journey to Empower Women in Cybersecurity

Finding My Voice: From Kenya to the Global Stage

I was born and raised in Kenya, the youngest of three and the only daughter. My parents, who never had the chance to finish their education, sacrificed everything to give me opportunities they never had. Their courage became my foundation.

At sixteen, my mother signed me up to speak at a community event, without telling me first! I stood before 500 people and spoke about something that had long bothered me: there were no women on our community board. That same year, two women were appointed for the first time in our community’s history. This year, I was given the recognition for being a Community Leader at the Global Gujrati Gaurav Awards in BC for my work in educating seniors on cyber safety and helping many immigrants secure jobs.

I didn’t realize it then, but that moment would define my purpose: to speak up for those whose voices aren’t always heard.

From Isolation to Empowerment

When I moved to the UK to study Financial Economics, I faced a different kind of challenge - isolation. My accent made me stand out, and not always in a good way. There were times I felt invisible, even rejected. But I made a promise to myself in those lonely moments that no one else should feel the same way.

Years later, as a founding member of WiCyS Western Affiliate, I helped redesign how networking happens at cybersecurity events. Instead of leaving it to chance, we introduced structured networking that ensured everyone left with at least one new connection. It was a small change, but it made a big difference. Today, that format has been adopted by organizations like ISC2 and ISACA, creating spaces where every person feels they belong. 

Breaking Barriers and Building SHE

When I pivoted into cybersecurity sales after moving to Canada, I encountered another wall. I applied for a senior role and failed a personality test, one that unfairly filtered out many talented women. I refused to accept that. I focused on listening, solving real customer challenges, and eventually became the top seller. That success helped eliminate the test altogether, opening doors for many more women who came after me. That experience planted a seed that would grow into one of my proudest initiatives: SHE (Sharing Her Empowerment).

It started as a simple fireside chat on diversity and inclusion - just 40 seats over lunch. Within minutes of sending the invite, we had 90 people signed up. Executives moved us into a larger room, and that event changed everything. SHE became our first employee resource group focused on empowering women, increasing representation in leadership, and amplifying women’s voices within the organization. Even with just 19% women, we created a ripple effect that reached the boardroom and beyond.

SHE showed me that when women stand together, transformation happens.

Creating Pathways for the Next Generation

Mentorship has always been close to my heart. During the pandemic, I met incredible women, who were trying to break into cybersecurity but kept facing barriers. I challenged hiring norms, advocated for fair opportunities, and helped launch internship programs that gave women hands-on experience. Today, many of them are thriving in their cyber careers, a true reflection of what’s possible when we lift as we climb.

Through Standout to Lead, I partnered with Women Get On Board to help women in cybersecurity gain board seats. Watching more women step into decision-making roles reminds me that leadership isn’t about titles, it’s about creating pathways for others.

Women in Cybersecurity: Our Collective Story

This year, I’m deeply honored to be named among the Top 20 Cybersecurity Women of the World by the United Cybersecurity Alliance. Their mission - to empower women, elevate diverse voices, and drive equity in our field, mirrors everything I believe in.

I’m also thrilled to be part of the upcoming documentary premiere, “The WOMEN IN SECURITY Documentary,” proudly sponsored by Sentra, Amazon WWOS, and Pinkerton among others. This film shines a light on the fearless women redefining what leadership looks like in our industry.

As a member of Sentra’s community, I see the same commitment to visibility, inclusion, and impact that has guided my journey. Together, we’re not just securing data, we’re securing the future of those who will lead next.

Asante Sana – Thank You

My story, my safari, is still being written. I’ve learned that impact doesn’t come from perfection, but from purpose. Whether it’s advocating for fairness, mentoring the next generation, or sharing our stories, every step we take matters.

To every woman, every underrepresented voice in STEM, and everyone who’s ever felt unseen - stay authentic, speak up, and don’t be afraid of the outcome. You might just change the world.

Join me and the Sentra team at The WOMEN IN SECURITY Documentary Premiere, a celebration of leadership, resilience, and the voices shaping the future of our industry.

Save your seat at The Women in Security premiere here (spots are limited).

Follow Sentra on LinkedIn and YouTube for more updates on the event and stories that inspire change.

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