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Enhancing AI Governance: The Crucial Role of Data Security

November 17, 2024
5
 Min Read
AI and ML

In today’s hyper-connected world, where big data powers decision-making, artificial intelligence (AI) is transforming industries and user experiences around the globe. Yet, while AI technology brings exciting possibilities, it also raises pressing concerns, particularly related to security, compliance, and ethical integrity. 

As AI adoption accelerates一fueled by increasingly vast and unstructured data sources—organizations seeking to secure AI deployments (and investments) must establish a strong AI governance initiative with data governance at its core.

This article delves into the essentials of AI governance, outlines its importance, examines the challenges involved, and presents best practices to help companies implement a resilient, secure, and ethically sound AI governance framework centered around data.

What is AI Governance?

AI governance encompasses the frameworks, practices, and policies that guide the responsible, safe, and ethical use of AI systems across an organization. Effective AI governance integrates technical elements—data, models, and code—with human oversight for a holistic framework that evolves alongside an organization’s AI initiatives.

Embedding AI governance, along with related data security measures, into organizational practices not only guarantees responsible AI use but also long-term success in an increasingly AI-driven world.

With an AI governance structure rooted in secure data practices, your company can:

  • Mitigate risks: Ongoing AI risk assessments can proactively identify and address potential threats, such as algorithmic bias, transparency gaps, and potential data leakage; this ensures fairer AI outcomes while minimizing reputational and regulatory risks tied to flawed or opaque AI systems.
  • Ensure strict adherence: Effective AI governance and compliance policies create clear accountability structures, aligning AI deployments and data use with both internal guidelines and the broader regulatory landscape such as data privacy laws or industry-specific AI standards.
  • Optimize AI performance: Centralized AI governance provides full visibility into your end-to-end AI deployments一from data sources and engineered feature sets to trained models and inference endpoints; this facilitates faster and more reliable AI innovations while reducing security vulnerabilities.
  • Foster trust: Ethical AI governance practices, backed by strict data security, reinforce trust by ensuring AI systems are transparent and safe, which is crucial for building confidence among both internal and external stakeholders.

A robust AI governance framework means your organization can safeguard sensitive data, build trust, and responsibly harness AI’s transformative potential, all while maintaining a transparent and aligned approach to AI.

Why Data Governance Is at the Center of AI Governance

Data governance is key to effective AI governance because AI systems require high-quality, secure data to properly function. Accurate, complete, and consistent data is a must for AI performance and the decisions that guide it. Additionally, strong data governance enables organizations to navigate complex regulatory landscapes and mitigate ethical concerns related to bias.

Through a structured data governance framework, organizations can not only achieve compliance but also leverage data as a strategic asset, ultimately leading to more reliable and ethical AI outcomes.

Risks of Not Having a Data-Driven AI Governance Framework

AI systems are inherently complex, non-deterministic, and highly adaptive—characteristics that pose unique challenges for governance. 

Many organizations face difficulty blending AI governance with their existing data governance and IT protocols; however, a centralized approach to governance is necessary for comprehensive oversight. Without a data-centric AI governance framework, organizations face risks such as:

  • Opaque decision-making: Without clear lineage and governance, it becomes difficult to trace and interpret AI decisions, which can lead to unethical, discriminatory, or harmful outcomes.
  • Data breaches: AI systems rely on large volumes of data, making rigorous data security protocols essential to avoid leaks of sensitive information across an extended attack surface covering both model inputs and outputs. 
  • Regulatory non-compliance: The fast-paced evolution of AI regulations means organizations without a governance framework risk large penalties for non-compliance and potential reputational damage. 

For more insights on managing AI and data privacy compliance, see our tips for security leaders.

Implementing AI Governance: A Balancing Act

While centralized, robust AI governance is crucial, implementing it successfully poses significant challenges. Organizations must find a balance between driving innovation and maintaining strict oversight of AI operations.

A primary issue is ensuring that governance processes are both adaptable enough to support AI innovation and stringent enough to uphold data security and regulatory compliance. This balance is difficult to achieve, particularly as AI regulations vary widely across jurisdictions and are frequently updated. 

Another key challenge is the demand for continuous monitoring and auditing. Effective governance requires real-time tracking of data usage, model behavior, and compliance adherence, which can add significant operational overhead if not managed carefully.

To address these challenges, organizations need an adaptive governance framework that prioritizes privacy, data security, and ethical responsibility, while also supporting operational efficiency and scalability.

Frameworks & Best Practices for Implementing Data-Driven AI Governance

While there is no universal model for AI governance, your organization can look to established frameworks, such as the AI Act or OECD AI Principles, to create a framework tailored to your own risk tolerance, industry regulations, AI use cases, and culture.

Below we explore key data-driven best practices—relevant across AI use cases—that can best help you structure an effective and secure data-centric AI governance framework.

Adopt a Lifecycle Approach

A lifecycle approach divides oversight into stages. Implementing governance at each stage of the AI lifecycle enables thorough oversight of projects from start to finish following a multi-layered security strategy. 

For example, in the development phase, teams can conduct data risk assessments, while ongoing performance monitoring ensures long-term alignment with governance policies and control over data drift.

Prioritize Data Security

Protecting sensitive data is foundational to responsible AI governance. Begin by achieving full visibility into data assets, categorize them by relevance, and then assign risk scores to prioritize security actions. 

An advanced data risk assessment combined with data detection and response (DDR) can help you streamline risk scoring and threat mitigation across your entire data catalog, ensuring a strong data security posture.

Adopt a Least Privilege Access Model

Restricting data access based on user roles and responsibilities limits unauthorized access and aligns with a zero-trust security approach. By ensuring that sensitive data is accessible only to those who need it for their work via least privilege, you reduce the risk of data breaches and enhance overall data security.

Establish Data Quality Monitoring

Ongoing data quality checks help maintain data integrity and accuracy, meaning AI systems will be trained on high-quality data sets and serve quality requests. 

Implement processes for continuous monitoring of data quality and regularly assess data integrity and accuracy; this will minimize risks associated with poor data quality and improve AI performance by keeping data aligned with governance standards.

Implement AI-Specific Detection and Response Mechanisms

Continuous monitoring of AI systems for anomalies in data patterns or performance is critical for detecting risks before they escalate. 

Anomaly detection for AI deployments can alert security teams in real time to unusual access patterns or shifts in model performance. Automated incident response protocols guarantee quick intervention, maintaining AI output integrity and protecting against potential threats.

A data security posture management (DSPM) tool allows you to incorporate continuous monitoring with minimum overhead to facilitate proactive risk management.

Conclusion

AI governance is essential for responsible, secure, and compliant AI deployments. By prioritizing data governance, organizations can effectively manage risks, enhance transparency, and align with ethical standards while maximizing the operational performance of AI.

As AI technology evolves, governance frameworks must be adaptive, ready to address advancements such as generative AI, and capable of complying with new regulations, like the UK GDPR.

To learn how Sentra can streamline your data and AI compliance efforts, explore our guide on data security posture management (DSPM). Or, see Sentra in action today by signing up for a demo.

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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Yoav Regev
Yoav Regev
January 15, 2025
3
Min Read

The Importance of Data Security for Growth: A Blueprint for Innovation

The Importance of Data Security for Growth: A Blueprint for Innovation

“For whosoever commands the sea commands the trade; whosoever commands the trade of the world commands the riches of the world, and consequently the world itself.” — Sir Walter Raleigh.

For centuries, power belonged to those who ruled the seas. Today, power belongs to those who control and harness their data’s potential. But let’s face it—many organizations are adrift, overwhelmed by the sheer volume of data and rushing to keep pace in a rapidly shifting threatscape. Navigating these waters requires clarity, foresight, and the right tools to stay afloat and steer toward success. Sound familiar? 

In this new reality, controlling data now drives success. But success isn’t just about collecting data, it’s about being truly data-driven. For modern businesses, data isn’t just another resource. Data is the engine of growth, innovation, and smarter decision-making. Yet many leaders still grapple with critical questions:

  • Are you really in control of your data?
  • Do you make decisions based on the insights your data provides?
  • Are you using it to navigate toward long-term success?

In this blog, I’ll explore why mastering your data isn’t just a strategic advantage—it’s the foundation of survival in today’s competitive market - Data is the way to success and prosperity in an organization. I’ll also break down how forward-thinking organizations are using comprehensive Data Security Platforms to navigate this new era where speed, innovation, and security can finally coexist.

The Role of Data in Organizational Success

Data drives innovation, fuels growth, and powers smart decision-making. Businesses use data to develop new products, improve customer experiences, and maintain a competitive edge. But let’s be clear, collecting vast amounts of data isn’t enough. True success comes from securing it, understanding it, and putting it to work effectively.

If you don’t fully understand or protect your data, how valuable can it really be?

Organizations face a constant barrage of threats: data breaches, shadow data, and excessive access permissions. Without strong safeguards, these vulnerabilities don’t just pose risks—they become ticking time bombs.

For years, controlling and understanding your data was impossible—it was a complex, imprecise, expensive, and time-consuming process that required significant resources. Today, for the first time ever, there is a solution. With innovative approaches and cutting-edge technology, organizations can now gain the clarity and control they need to manage their data effectively!

With the right approach, businesses can transform their data management from a reactive process to a competitive advantage, driving both innovation and resilience. As data security demands grow, these tools have evolved into something much more powerful: comprehensive Data Security Platforms (DSPs). Unlike basic solutions, you can expect a data security platform to deliver advanced capabilities such as enhanced access control, real-time threat monitoring, and holistic data management. This all-encompassing approach doesn’t just protect sensitive data—it makes it actionable and valuable, empowering organizations to thrive in an ever-changing landscape.

Building a strong data security strategy starts with visionary leadership. It’s about creating a foundation that not only protects data but enables organizations to innovate fearlessly in the face of uncertainty.

The Three Key Pillars for Securing and Leveraging Data

1. Understand Your Data

The foundation of any data security strategy is visibility. Knowing where your data is stored, who has access to it, and what sensitive information it contains is essential. Data sprawl remains a challenge for many organizations. The latest tools, powered by automation and intelligence, provide unprecedented clarity by discovering, classifying, and mapping sensitive data. These insights allow businesses to make sharper, faster decisions to protect and harness their most valuable resource.

Beyond discovery, advanced tools continuously monitor data flows, track changes, and alert teams to potential risks in real-time. With a complete understanding of their data, organizations can shift from reactive responses to proactive management.

2. Control Your Data

Visibility is the first step; control is the next. Managing access to sensitive information is critical to minimizing risk. This involves identifying overly broad permissions and ensuring that access is granted only to those who truly need it.

Having full control of your data becomes even more challenging when data is copied or moved between environments—such as from private to public or from encrypted to unencrypted. This process creates "similar data," in which data that was initially secure becomes exposed to greater risk by being moved into a lower environment. Data that was once limited to a small, regulated group of identities (users) then becomes accessible by a larger number of users, resulting in a significant loss of control.

Effective data security strategies go beyond identifying these issues. They enforce access policies, automate corrective actions, and integrate with identity and access management systems to help organizations maintain a strong security posture, even as their business needs change and evolve. In addition to having robust data identification methods, it’s crucial to prioritize the implementation of access control measures. This involves establishing Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC) policies, so that the right users have permissions at the right times.

3. Monitor Your Data

Real security goes beyond awareness—it demands a dynamic approach. Real-time monitoring doesn’t just detect risks and threats; it anticipates them. By spotting unusual behaviors or unauthorized access early, businesses can preempt incidents and maintain trust in an increasingly volatile digital environment. Advanced tools provide visibility into suspicious activities, offer real-time alerts, and automate responses, enabling security teams to act swiftly. This ongoing oversight ensures that businesses stay resilient and adaptive in an ever-changing environment.

Being Fast and Secure

In today’s competitive market, speed drives success—but speed without security is a recipe for disaster. Organizations must balance rapid innovation with robust protection.

Modern tools streamline security operations by delivering actionable insights for faster, more informed risk responses. A comprehensive Data Security Platform goes further by integrating security workflows, automating threat detection, and enabling real-time remediation across multi-cloud environments. By embedding security into daily processes, businesses can maintain agility while protecting their most critical assets.

Why Continuous Data Security is the Key to Long-Term Growth

Data security isn’t a one-and-done effort—it’s an ongoing commitment. As businesses scale and adopt new technologies, their data environments grow more complex, and security threats continue to evolve. Organizations that continuously understand and control their data are poised to turn uncertainty into opportunity. By maintaining this control, they sustain growth, protect trust, and future-proof their success.

Adaptability is the foundation of long-term success. A robust data security platform evolves with your business, providing continuous visibility, automating risk management, and enabling proactive security measures. By embedding these capabilities into daily operations, organizations can maintain speed and agility without compromising protection.

In today’s data-driven world, success hinges on making informed decisions with secure data. Businesses that master continuous data security will not only safeguard their assets but also position themselves to thrive in an ever-changing competitive landscape.

Conclusion: The Critical Link Between Data Security and Success

Data is the lifeblood of modern businesses, driving growth, innovation, and decision-making. But with this immense value comes an equally immense responsibility: protecting it. A comprehensive data security platform goes beyond the basics, unifying discovery, classification, access governance, and real-time protection into a single proactive approach. True success in a data-driven world demands more than agility—it requires mastery. Organizations that embrace data security as a catalyst for innovation and resilience are the ones who will lead the way in today’s competitive landscape.

The question is: Will you lead the charge or risk being left behind? The opportunity to secure your future starts now.

Final thought: In my work with organizations across industries, I’ve seen firsthand how those who treat data security as a strategic enabler, rather than an obligation, consistently outperform their peers. The future belongs to those who lead with confidence, clarity, and control.

If you're interested in learning how Sentra's Data Security Platform can help you understand and protect your data to drive success in today’s competitive landscape, request a demo today.

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Yair Cohen
Yair Cohen
January 13, 2025
4
Min Read
Data Security

Automating Sensitive Data Classification in Audio, Image and Video Files

Automating Sensitive Data Classification in Audio, Image and Video Files

The world we live in is constantly changing. Innovation and technology are advancing at an unprecedented pace. So much innovation and high tech. Yet, in the midst of all this progress, vast amounts of critical data continue to be stored in various formats, often scattered across network file shares network file shares or cloud storage. Not just structured documents—PDFs, text files, or PowerPoint presentations - we're talking about audio recordings, video files, x-ray images, engineering charts, and so much more.

How do you truly understand the content hidden within these formats? 

After all, many of these files could contain your organization’s crown jewels—sensitive data, intellectual property, and proprietary information—that must be carefully protected.

Importance of Extracting and Understanding Unstructured Data

Extracting and analyzing data from audio, image and video files is crucial in a data-driven world. Media files often contain valuable and sensitive information that, when processed effectively, can be leveraged for various applications.

  • Accessibility: Transcribing audio into text helps make content accessible to people with hearing impairments and improves usability across different languages and regions, ensuring compliance with accessibility regulations.
  • Searchability: Text extraction enables indexing of media content, making it easier to search and categorize based on keywords or topics. This becomes critical when managing sensitive data, ensuring that privacy and security standards are maintained while improving data discoverability.
  • Insights and Analytics: Understanding the content of audio, video, or images can help derive actionable insights for fields like marketing, security, and education. This includes identifying sensitive data that may require protection, ensuring compliance with privacy regulations, and protecting against unauthorized access.
  • Automation: Automated analysis of multimedia content supports workflows like content moderation, fraud detection, and automated video tagging. This helps prevent exposure of sensitive data and strengthens security measures by identifying potential risks or breaches in real-time.
  • Compliance and Legal Reasons: Accurate transcription and content analysis are essential for meeting regulatory requirements and conducting audits, particularly when dealing with sensitive or personally identifiable information (PII). Proper extraction and understanding of media data help ensure that organizations comply with privacy laws such as GDPR or HIPAA, safeguarding against data breaches and potential legal issues.

Effective extraction and analysis of media files unlocks valuable insights while also playing a critical role in maintaining robust data security and ensuring compliance with evolving regulations.

Cases Where Sensitive Data Can Be Found in Audio & MP4 Files

In industries such as retail and consumer services, call centers frequently record customer calls for quality assurance purposes. These recordings often contain sensitive information like personally identifiable information (PII) and payment card data (PCI), which need to be safeguarded. In the media sector, intellectual property often consists of unpublished or licensed videos, such as films and TV shows, which are copyrighted and require protection with rights management technology. However, it's common for employees or apps to extract snippets or screenshots from these videos and store them on personal drives or in unsecured environments, exposing valuable content to unauthorized access.

Another example is when intellectual property or trade secrets are inadvertently shared through unsecured audio or video files, putting sensitive business information at risk - or simply a leakage of confidential information such as non-public sales figures for a publicly traded company. Serious damage can occur to a public company if a bad actor got a hold of an internal audio or video call recording in advance where forecasts or other non-public sales figures are discussed. This would likely be a material disclosure requiring regulatory reporting (ie., for SEC 4-day material breach compliance).

Discover Sensitive Data in MP4s and Audio with Sentra

AI-powered technologies that extract text from images, audio, and video are built on advanced machine learning models like Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR)

OCR converts visual text in images or videos into editable, searchable formats, while ASR transcribes spoken language from audio and video into text. These systems are fueled by deep learning algorithms trained on vast datasets, enabling them to recognize diverse fonts, handwriting, languages, accents, and even complex layouts. At scale, cloud computing enables the deployment of these AI models by leveraging powerful GPUs and scalable infrastructure to handle high volumes of data efficiently. 

The Sentra Cloud-Native Platform integrates tools like serverless computing, distributed processing, and API-driven architectures, allowing it to access these advanced capabilities that run ML models on-demand. This seamless scaling capability ensures fast, accurate text extraction across the global user base.

Sentra is rapidly adopting advancements in AI-driven text extraction. A few examples of recent advancements are Optical Character Recognition (OCR) that works seamlessly on dynamic video streams and robust Automatic Speech Recognition (ASR) models capable of transcribing multilingual and domain-specific content with high accuracy. Additionally, innovations in pre-trained transformer models, like Vision-Language and Speech-Language models, enable context-aware extractions, such as identifying key information from complex layouts or detecting sentiment in spoken text. These breakthroughs are pushing the boundaries of accessibility and automation across industries, and enable data security and privacy teams to achieve what was previously thought impossible.

Large volume of sensitive data was copied into a shared drive
Data at Risk - Data Activity Overview

Sentra: An Innovator in Sensitive Data Discovery within Video & Audio

Sentra’s innovative approach to sensitive data discovery goes beyond traditional text-based formats, leveraging advanced ML and AI algorithms to extract and classify data from audio, video, and images. Extracting and understanding unstructured data from media files is increasingly critical in today’s data-driven world. These files often contain valuable and sensitive information that, when properly processed, can unlock powerful insights and drive better decision-making across industries. Sentra’s solution contextualizes multimedia content to highlight what matters most for your unique needs, delivering instant answers with a single click—capabilities we believe set us apart as the only DSPM solution offering this level of functionality.

As threats continue to evolve across multiple vectors, including text, audio, and video—solution providers must constantly adopt new techniques for accurate classification and detection. AI plays a critical role in enhancing these capabilities, offering powerful tools to improve precision and scalability. Sentra is committed to driving innovation by leveraging these advanced technologies to keep data secure.

Want to see it in action? Request a demo today and discover how Sentra can help you protect sensitive data wherever it resides, even in image and audio formats.

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Team Sentra
Team Sentra
December 9, 2024
3
Min Read
Data Security

8 Holiday Data Security Tips for Businesses

8 Holiday Data Security Tips for Businesses

As the end of the year approaches and the holiday season brings a slight respite to many businesses, it's the perfect time to review and strengthen your data security practices. With fewer employees in the office and a natural dip in activity, the holidays present an opportunity to take proactive steps that can safeguard your organization in the new year. From revisiting access permissions to guarding sensitive data access during downtime, these tips will help you ensure that your data remains protected, even when things are quieter.

Here's how you can bolster your business’s security efforts before the year ends:

  1. Review Access and Permissions Before the New Year
    Take advantage of the holiday downtime to review data access permissions in your systems. Ensure employees only have access to the data they need, and revoke permissions for users who no longer require them (or worse, are no longer employees). It's a proactive way to start the new year securely.
  2. Limit Access to Sensitive Data During Holiday Downtime
    With many staff members out of the office, review who has access to sensitive data. Temporarily restrict access to critical systems and data for those not on active duty to minimize the risk of accidental or malicious data exposure during the holidays.
  3. Have a Data Usage Policy
    With the holidays bringing a mix of time off and remote work, it’s a good idea to revisit your data usage policy. Creating and maintaining a data usage policy ensures clear guidelines for who can access what data, when, and how, especially during the busy holiday season when staff availability may be lower. By setting clear rules, you can help prevent unauthorized access or misuse, ensuring that your data remains secure throughout the holidays, and all the way to 2025.
  4. Eliminate Unnecessary Data to Reduce Shadow Data Risks
    Data security risks increase as long as data remains accessible. With the holiday season bringing potential distractions, it's a great time to review and delete any unnecessary sensitive data, such as PII or PHI, to prevent shadow data from posing a security risk as the year wraps up with the new year approaching.
  5. Apply Proper Hygiene to Protect Sensitive Data
    For sensitive data that must exist, be certain to apply proper hygiene such as masking/de-identification, encryption, logging, etc., to ensure the data isn’t improperly disclosed. With holiday sales, year-end reporting, and customer gift transactions in full swing, ensuring sensitive data is secure is more important than ever. Many stores have native tools that can assist (e.g., Snowflake DDM, Purview MIP, etc.).
  6. Monitor Third-Party Data Access
    Unchecked third-party access can lead to data breaches, financial loss, and reputational damage. The holidays often mean new partnerships or vendors handling seasonal activities like marketing campaigns or order fulfillment. Keep track of how vendors collect, use, and share your data. Create an inventory of vendors and map their data access to ensure proper oversight, especially during this busy time.
  7. Monitor Data Movement and Transformations
    Data is dynamic and constantly on the move. Monitor whenever data is copied, moved from one environment to another, crosses regulated perimeters (e.g., GDPR), or is ETL-processed, as these activities may introduce new sensitive data vulnerabilities. The holiday rush often involves increased data activity for promotions, logistics, and end-of-year tasks, making it crucial to ensure new data locations are secure and configurations are correct.
  8. Continuously Monitor for New Data Threats
    Despite our best protective measures, bad things happen. A user’s credentials are compromised. A partner accesses sensitive information. An intruder gains access to our network. A disgruntled employee steals secrets. The holiday season’s unique pressures and distractions increase the likelihood of these incidents. Watch for anomalies by continually monitoring data activity and alerting whenever suspicious things occur—so you can react swiftly to prevent damage or leakage, even amid the holiday bustle. A user’s credentials are compromised. A partner accesses sensitive information. An intruder gains access to our network. A disgruntled employee steals secrets. Watch for these anomalies by continually monitoring data activity and alerting whenever suspicious things occur - so you can react swiftly to prevent damage or leakage.

Wrapping Up the Year with Stronger Data Security

By taking the time to review and update your data security practices before the year wraps up, you can start the new year with confidence, knowing that your systems are secure and your data is protected. Implementing these simple but effective measures will help mitigate risks and set a strong foundation for 2025. Don't let the holiday season be an excuse for lax security - use this time wisely to ensure your organization is prepared for any data security challenges the new year may bring.

Visit Sentra's demo page to learn more about how you can ensure your organization can stay ahead and start 2025 with a stronger data security posture.

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