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GDPR Compliance Failures Lead to Surge in Fines

September 16, 2024
4
 Min Read
Compliance

In recent years, the landscape of data privacy and protection has become increasingly stringent, with regulators around the world cracking down on companies that fail to comply with local and international standards. 

The latest high-profile case involves Uber, which was recently fined a staggering €290 million ($324 million) by the Dutch Data Protection Authority (DPA) for violations related to the General Data Protection Regulation (GDPR). This is a wake up call for multinational companies. 

Graph showing the rise of GDPR fines from 2018-2024

What is GDPR?

The General Data Protection Regulation (GDPR) is a data protection law that came into effect in the EU in May 2018. Its goal is to give individuals more control over their personal data and unify data protection rules across the EU.

GDPR gives extra protection to special categories of sensitive data. Both 'controllers' (who decide how data is processed) and 'processors' (who act on their behalf) must comply. Joint controllers may share responsibility when multiple entities manage data.

Who Does the GDPR Apply To?

GDPR applies to both EU-based and non-EU organizations that handle the data of EU residents. The regulation requires organizations to get clear consent for data collection and processing, and it gives individuals rights to access, correct, and delete their data. Organizations must also ensure strong data security and report any data breaches promptly.

What Are the Penalties for Non-Compliance with GDPR?

Non-compliance with the General Data Protection Regulation (GDPR) can result in substantial penalties.

Article 83 of the GDPR establishes the fine framework, which includes the following:

Maximum Fine: The maximum fine for GDPR non-compliance can reach up to 20 million euros, or 4% of the company’s total global turnover from the preceding fiscal year, whichever is higher.

Alternative Penalty: In certain cases, the fine may be set at 10 million euros or 2% of the annual global revenue, as outlined in Article 83(4).

Additionally, individual EU member states have the authority to impose their own penalties for breaches not specifically addressed by Article 83, as permitted by the GDPR’s flexibility clause.

So far, the maximum fine given under GDPR was to Meta in 2023, which was fined $1.3 billion for violating GDPR laws related to data transfers. We’ll delve into the details of that case shortly.

Can Individuals Be Fined for GDPR Breaches?

While fines are typically imposed on organizations, individuals can be fined under certain circumstances. For example, if a person is self-employed and processes personal data as part of their business activities, they could be held responsible for a GDPR breach. However, UK-GDPR and EU-GDPR do not apply to data processing carried out by individuals for personal or household activities. 

According to GDPR Chapter 1, Article 4, “any natural or legal person, public authority, agency, or body” can be held accountable for non-compliance. This means that GDPR regulations do not distinguish significantly between individuals and corporations when it comes to breaches.

Specific scenarios where individuals within organizations may be fined include:

  • Obstructing a GDPR compliance investigation.
  • Providing false information to the ICO or DPA.
  • Destroying or falsifying evidence or information.
  • Obstructing official warrants related to GDPR or privacy laws.
  • Unlawfully obtaining personal data without the data controller's permission.

The Top 3 GDPR Fines and Their Impact

1.  Meta - €1.2 Billion ($1.3 Billion), 2023 

In May 2023, Meta, the U.S. tech giant, was hit with a staggering $1.3 billion fine by an Irish court for violating GDPR regulations concerning data transfers between the E.U. and the U.S. This massive penalty came after the E.U.-U.S. Privacy Shield Framework, which previously provided legal cover for such transfers, was invalidated in 2020. The court found that the framework failed to offer sufficient protection for EU citizens against government surveillance. This fine now stands as the largest ever under GDPR, surpassing Amazon’s 2021 record.

2. Amazon - €746 million ($781 million), 2021

Which leads us to Amazon at number 2, not bad. In 2021, Amazon Europe received the second-largest GDPR fine to date from Luxembourg’s National Commission for Data Protection (CNPD). The fine was imposed after it was determined that the online retailer was storing advertisement cookies without obtaining proper consent from its users.

3. Instagram - €405 million ($427 million), 2022

The Irish Data Protection Commission (DPC) fined Instagram for violating children’s privacy online in September 2022. The violations included the public exposure of kids' phone numbers and email addresses. The DPC found that Instagram’s user registration system could default child users' accounts to "public" instead of "private," contradicting GDPR’s privacy by design principles and the regulations aimed at safeguarding children's information.

Uber currently ranks at number 6 with the latest €290 million fine they received from the Dutch Data Protection Authority (DPA) for the GDPR related violations.

Uber’s GDPR Violation

The Dutch DPA accused Uber of transferring sensitive data of European drivers to the United States without implementing appropriate safeguards. This included personal information such as account details, location data, payment information, and even sensitive documents like taxi licenses, criminal records, and medical data. The failure to protect this data adequately, especially after the invalidation of the E.U.-U.S. Privacy Shield in 2020, constituted a serious violation of GDPR.

Despite Uber's claim that its cross-border data transfer process was compliant with GDPR, the DPA's decision to impose the record fine underscores the growing importance of adhering to stringent data protection regulations. Uber has since ceased the practice, but the financial and reputational damage is already done.

The Implications for Global Companies

The growing frequency of such fines sends a clear message to global companies: compliance with data protection regulations is non-negotiable. As European regulators continue to enforce GDPR rigorously, companies that fail to implement adequate data protection measures risk facing severe financial penalties and reputational harm.

In the case of Uber, the company’s failure to use appropriate mechanisms for data transfers, such as Standard Contractual Clauses, led to significant repercussions. This situation emphasizes the importance of staying current with regulatory changes, such as the introduction of the E.U.-U.S. Data Privacy Framework, and ensuring that all data transfer practices are fully compliant.

How Sentra Helps Orgs Stay Compliant with GDPR

Sentra helps organizations maintain GDPR compliance by effectively tagging data belonging to European citizens.

When EU citizens' Personally Identifiable Information (PII) is moved or stored outside of EU data centers, Sentra will detect and alert you in near real-time. Our continuous monitoring and scanning capabilities ensure that any data violations are identified and flagged promptly.

Example of EU citizens PII stored outside of EU data centers

Unlike traditional methods where data replication can obscure visibility and lead to issues during audits, Sentra provides ongoing visibility into data storage. This proactive approach significantly reduces the risk by alerting you to potential compliance issues as they arise.

Sentra does automatic classification of localized data - specifically in this case, EU data. Below you can see an example of how we do this. 

Sentra's automatic classification of localized data

The Rise of Compliance Violations: A Wake-up Call

The increasing number of compliance violations and the related hefty fines should serve as a wake-up call for companies worldwide. As the regulatory environment becomes more complex, it is crucial for organizations to prioritize data protection and privacy. By doing so, they can avoid costly penalties and maintain the trust of their customers and stakeholders.

Solutions such as Sentra provide a cost-effective means to ensure sensitive data always has the right posture and security controls - no matter where the data travels - and can alert on exceptions that require rapid remediation. In this way, organizations can remain regulatory compliant, avoid the steep penalties for violations, and ensure the proper, secure use of data throughout their ecosystem.

To learn more about how Sentra's Data Security Platform can help you stay compliant, avoid GDPR penalties, and ensure the proper, secure use of data, request a demo today. 

Meni is an experienced product manager and the former founder of Pixibots (A mobile applications studio). In the past 15 years, he gained expertise in various industries such as: e-commerce, cloud management, dev-tools, mobile games, and more. He is passionate about delivering high quality technical products, that are intuitive and easy to use.

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Ran Shister
Ran Shister
March 27, 2025
3
Min Read
Sentra Case Study

Empowering Users to Self-Protect Their Data

Empowering Users to Self-Protect Their Data

In today’s fast-evolving cybersecurity landscape, organizations must not only deploy sophisticated security tools but also empower users to self-protect. Operationalizing this data security requires a proactive approach that integrates automation, streamlined processes, and user education. A recent discussion with Sapir Gottdiner, Cyber Security Architect at Global-e, highlighted key strategies to enhance data security by addressing alert management, sensitive data exposure, and user-driven security measures.

As a provider of end-to-end e-commerce solutions that combine localization capabilities, business intelligence, and logistics for streamlined international expansion, Global-e makes cross-border sales as simple as domestic ones. The chosen partner of leading brands and retailers across the USA, Europe and Asia, Global-e sets the standard of global e-commerce. This requires a strong commitment to security and compliance, and Global-e must comply with a number of strict regulations.

Automating Security Tasks for Efficiency

“One of the primary challenges faced by any security team is keeping pace with the volume of security alerts and the effort required to address them”, said Sapir. Automating human resource-constrained tasks is crucial for efficiency. For example, sensitive data should only exist in certain controlled environments, as improper data handling can lead to vulnerabilities. By leveraging DSPM which acts as a validation tool, organizations can automate the detection of sensitive information stored in incorrect locations and initiate remediation processes without human intervention.

Strengthening Sensitive Data Protection

A concern identified in the discussion was data accessible to unauthorized personnel in Microsoft OneDrive, that may contain sensitive information. To mitigate this, organizations should automate the creation of support tickets (in Jira, for instance) for security incidents, ensuring critical and high-risk alerts are addressed immediately. Assigning these incidents to the relevant departments and data owners ensures accountability and prompt resolution. Additionally, identifying the type and location of sensitive data enables organizations to implement precise fixes, reducing exposure risks.

Risk Management and Process Improvement

Permissioning is equally important and organizations must establish clear procedures and policies for managing authentication credentials. Different actions for different levels of risk to ensure no business interruption is applicable in most cases. This can vary from easy, quick access revocation for low-risk cases while requiring manual verification for critical credentials. Furthermore, proper data storage is an important protection factor, given sovereignty regulations, data proliferation, etc. Implementing well-defined data mapping strategies and systematically applying proper hygiene and ensuring correct locations will minimize security gaps. For the future, Sapir envisions smart data mapping within O365 and deeper integrations with automated remediation workflow tools to further enhance security posture.

Continuous Review and Training

Sapir also suggests that to ensure compliance and effective security management, organizations should conduct monthly security reviews. These reviews help define when to close or suppress alerts, preventing unnecessary effort on minor issues. Additionally, policies should align with infrastructure security and regulatory compliance requirements such as GDPR, PCI and SOC2. Expanding security training programs is another essential step, equipping users with the knowledge on proper storage and handling of controlled data and how to avoid common security missteps. Empowering users to self-police/self-remediate allows lean security teams to scale data protection operations more efficiently.

Enhancing Communication and Future Improvements

Streamlined communication between security platforms, such as Jira and Microsoft Teams, can significantly improve incident resolution. Automating alert closures based on predefined criteria will reduce the workload on security teams. Addressing existing bugs, such as shadow IT detection issues, will further refine security processes. By fostering a culture of proactive security and leveraging automation, organizations can empower users to self-protect, ensuring a robust defense against evolving cyber threats.

Operationalizing data security is an ongoing effort that blends automation, user education, and process refinement. By taking a strategic user-enablement approach, organizations can create a security-aware culture while minimizing risks and optimizing their security response. Since implementing Sentra’s DSPM solution, Global-e has seen significant improvement in the strength of its data security posture. The company is now able to protect its cloud data more effectively, saving its security, IT, DevOps and engineering teams time, and ensuring it remains compliant with regulatory requirements. Empowering users and data owners to take responsibility for their data security, and providing the right tools to do so easily, is a game changer to the organization.

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Meni Besso
Meni Besso
March 19, 2025
4
Min Read
Data Loss Prevention

Data Loss Prevention for Google Workspace

Data Loss Prevention for Google Workspace

We know that Google Workspace (formerly known as G Suite) and its assortment of services, including Gmail, Drive, Calendar, Meet, Docs, Sheets, Slides, Chat, and Vids, is a powerhouse for collaboration.

But the big question is: Do you know where your Google Workspace data is—and if it’s secure and who has access to it?

While Google Workspace has become an indispensable pillar in cloud operations and collaboration, its widespread adoption introduces significant security risks that businesses simply can't afford to ignore. To optimize Google Workspace data protection, enterprises must know how Google Workspace protects and classifies data. Knowing the scope, gaps, limitations, and silos of Google Workspace data protection mechanisms can help businesses strategize more effectively to mitigate data risks and ensure more holistic data security coverage across multi-cloud estates.

The Risks of Google Workspace Security

As with any dynamic cloud platform, Google Workspace is susceptible to data security risks, the most dangerous of which can do more than just undercut its benefits. Primarily, businesses should be concerned about the exposure of sensitive data nested within large volumes of unstructured data. For instance, if an employee shares a Google Drive folder or document containing sensitive data but with suboptimal access controls, it could snowball into a large-scale data security disaster. 

Without comprehensive visibility into sensitive data exposures across Google Workspace applications, businesses risk serious security threats. Besides sensitive data exposure, these include exploitable vulnerabilities, external attacks, human error, and shadow data. Complex shared responsibility models and unmet compliance policies also loom large, threatening the security of your data. 

To tackle these risks, businesses must prioritize and optimize data security across Google Workspace products while acknowledging that Google is rarely the sole platform an enterprise uses.

How Does Google Store Your Data?

To understand how to protect sensitive data in Google Workspace, it's essential to first examine how Google stores and manages this data. Why? Because the intricacies of data storage architectures and practices have significant implications for your security posture. 

Here are three-steps to help you understand and optimize your data storage in Google Workspace:

1. Know Where and How Google Stores Your Data

  • Google stores your files in customized servers in secure data centers.
  • Your data is automatically distributed across multiple regions, guaranteeing redundancy and availability.

2. Control Data Retention

  • Google retains your Workspace data until you or an admin deletes it.
  • Use Google Vault to manage retention policies and set custom retention rules for emails and files.
  • Regularly review and clean up unnecessary stored data to reduce security risks.

3. Secure Your Stored Data

  • Enable encryption for sensitive files in Google Drive.
  • Restrict who can view, edit, and share stored documents by implementing access controls.
  • Monitor data access logs to detect unauthorized access.

How Does Google Workspace Classify Your Data?

Google’s built-in classification tools are an acceptable starting point. However, they fall short of securing and classifying all unstructured data across complex cloud environments. This is because today's cloud attack surface expands across multiple providers, making security more complex than ever before. Consequently, Google's myopic classification often snowballs into bigger security problems, as data moves. Because of this evolving attack surface across multi-cloud environments, risk-ridden shadow data and unstructured data fester in Google Workspace apps. 

The Issue of Unstructured Data

It’s important to remember that most enterprise data is unstructured. Unstructured data refers to data that isn’t stored in standardized or easily manageable formats. In Google Workspace, this could be data in a Gmail draft, multimedia files in Google Drive, or other informal exchanges of sensitive information between Workspace apps. 

For years, unstructured data has been a nightmare for businesses to map, manage, and secure. Unstructured document stores and employee GDrives are hot zones for data risks. Native Google Drive data classification capabilities can be a useful source of metadata to support a more comprehensive external data classification solution. A cloud-native DSP solution can map, classify, and organize sensitive data, including PHI, PCI, and business secrets, across both Google Workspace and cloud platforms that Google's built-in capabilities do not cover, like AWS and S3.

How Does Google Workspace Protect Your Data?

Like its built-in classification mechanisms, Google's baseline security features, such as encryption and access controls, are good for simple use cases but aren't capable enough to fully protect complex environments. 

For both the classification and security of unstructured data, Google’s native tools may not suffice. A robust data loss prevention (DLP) solution should ideally do the trick for unstructured data. However, Google Workspace DLP alone and other protection measures (formerly referred to as G Suite data protection) are unlikely to provide holistic data security, especially in dynamic cloud environments.

Google Native Tool Challenges

Google’s basic protection measures don't tackle the full spectrum of critical Google Workspace data risks because they can't permeate unstructured documents, where sensitive data may reside in various protected states.

For example, an employee's personal Google Drive can potentially house exposed and exploitable sensitive data that can slip through Google's built-in security mechanisms. It’s also important to remember that Google Workspace data loss prevention capabilities do nothing to protect critical enterprise data hosted in other cloud platforms. 

Ultimately, while Google provides some security controls, they alone don’t offer the level of protection that today’s complex cloud environments demand. To close these gaps, businesses must look to complement Google’s built-in capabilities and invest in robust data security solutions.

Only a highly integrable data security tool with advanced AI and ML capabilities can protect unstructured data across Google Workspace’s diverse suite of apps, and further, across the entire enterprise data estate. This has become mandatory since multi-cloud architectures are the norm today.

A Robust Data Security Platform: The Key to Holistic Google Workspace Data Protection 

The speed, complexity, and rapid evolution of multi-cloud and hybrid cloud environments demand more advanced data security capabilities than Google Workspace’s native storage, classification, and protection features provide. 

It is becoming increasingly difficult to mitigate the risks associated with sensitive data.

To successfully remediate these risks, businesses urgently need robust data security posture management (DSPM) and data detection and response (DDR) solutions - preferably all in one platform. There's simply no other way to guarantee comprehensive data protection across Google Workspace. Furthermore, as mentioned earlier, most businesses don't exclusively use Google platforms. They often mix and match services from cloud providers like Google, Azure, and AWS.

In other words, besides limited data classification and protection, Google's built-in capabilities won't be able to extend into other branches of an enterprise's multi-cloud architecture. And having siloed data security tools for each of these cloud platforms increases costs and further complicates administration that can lead to critical coverage gaps. That's why the optimal solution is a holistic platform that can fill the gaps in Google's existing capabilities to provide unified data classification, security, and coverage across all other cloud platforms.

Sentra: The Ultimate Cloud-Agnostic Data Protection and Classification Solution 

To truly secure sensitive data across Google Workspace and beyond, enterprises need a cloud-native data security platform. That’s where Sentra comes in. It hands you enterprise-scale data protection by seamlessly integrating powerful capabilities like data discovery and classification, data security posture management (DSPM), data access governance (DAG), and data detection and response (DDR) into an all-in-one, easy-to-use platform.

By combining rule-based and large language model (LLM)-based classification, Sentra ensures accurate and scalable data security across Workspace apps like Google Drive—as well as data contained in apps from other cloud providers. This is crucial for any enterprise that hosts its data across disparate cloud platforms, not just Workspace. To classify unstructured data across these platforms, Sentra leverages supervised AI training models like BERT. It also uses zero-shot classification techniques to zero in on and accurately classify unstructured data. 

Sentra is particularly useful for anyone asking business-, industry-, or geography-specific data security questions such as “Does Google Workspace have HIPAA compliance frameworks?” and “Is my organization's use of Google Workspace GDPR-compliant?” The short answer to these questions: Integrate Sentra with your Google Workspace apps and you will see. 

Boost Your Google Workspace Data Protection with Sentra

By integrating Sentra with Google Workspace, companies can leverage AI-driven insights to distinguish employee data from customer data, ensuring a clearer understanding of their information landscape. Sentra also identifies customer-specific data types, such as personally identifiable information (PII), protected health information (PHI), product IDs, private codes, and localization requirements. Additionally, it detects toxic data combinations that may pose security risks.

Beyond insights, Sentra provides robust data protection through comprehensive inventorying and classification of unstructured data. It helps organizations right-size permissions, expose shadow data, and implement real-time detection of sensitive data exposure, security breaches, and suspicious activity, ensuring a proactive approach to data security.

No matter where your unstructured data resides, whether in Google Drive or any other cloud service, Sentra ensures it is accurately identified, classified, and protected with over 95% precision.

If you’re ready to take control of your data security, book a demo to discover how Sentra’s AI-driven protection secures your most valuable information across Google Workspace and beyond.

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Ron Reiter
Ron Reiter
March 4, 2025
4
Min Read
AI and ML

AI in Data Security: Guardian Angel or Trojan Horse?

AI in Data Security: Guardian Angel or Trojan Horse?

Artificial intelligence (AI) is transforming industries, empowering companies to achieve greater efficiency, and maintain a competitive edge. But here’s the catch: although AI unlocks unprecedented opportunities, its rapid adoption also introduces complex challenges—especially for data security and privacy. 

How do you accelerate transformation without compromising the integrity of your data? How do you harness AI’s power without it becoming a threat?

For security leaders, AI presents this very paradox. It is a powerful tool for mitigating risk through better detection of sensitive data, more accurate classification, and real-time response. However, it also introduces complex new risks, including expanded attack surfaces, sophisticated threat vectors, and compliance challenges.

As AI becomes ubiquitous and enterprise data systems become increasingly distributed, organizations must navigate the complexities of the big-data AI era to scale AI adoption safely. 

In this article, we explore the emerging challenges of using AI in data security and offer practical strategies to help organizations secure sensitive data.

The Emerging Challenges for Data Security with AI

AI-driven systems are driven by vast amounts of data, but this reliance introduces significant security risks—both from internal AI usage and external client-side AI applications. As organizations integrate AI deeper into their operations, security leaders must recognize and mitigate the growing vulnerabilities that come with it.

Below, we outline the four biggest AI security challenges that will shape how you protect data and how you can address them.

1. Expanded Attack Surfaces

AI’s dependence on massive datasets—often unstructured and spread across cloud environments—creates an expansive attack surface. This data sprawl increases exposure to adversarial threats, such as model inversion attacks, where bad actors can reverse-engineer AI models to extract sensitive attributes or even re-identify anonymized data.

To put this in perspective, an AI system trained on healthcare data could inadvertently leak protected health information (PHI) if improperly secured. As adversaries refine their techniques, protecting AI models from data leakage must be a top priority.

For a detailed analysis of this challenge, refer to NIST’s report,Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations.

2. Sophisticated and Evolving Threat Landscape

The same AI advancements that enable organizations to improve detection and response are also empowering threat actors. Attackers are leveraging AI to automate and enhance malicious campaigns, from highly targeted phishing attacks to AI-generated malware and deepfake fraud.

According to StrongDM's “The State of AI in Cybersecurity Report,” 65% of security professionals believe their organizations are unprepared for AI-driven threats. This highlights a critical gap: while AI-powered defenses continue to improve, attackers are innovating just as fast—if not faster. Organizations must adopt AI-driven security tools and proactive defense strategies to keep pace with this rapidly evolving threat landscape.

3. Data Privacy and Compliance Risks

AI’s reliance on large datasets introduces compliance risks for organizations bound by regulations such as GDPR, CCPA, or HIPAA. Improper handling of sensitive data within AI models can lead to regulatory violations, fines, and reputational damage. One of the biggest challenges is AI’s opacity—in many cases, organizations lack full visibility into how AI systems process, store, and generate insights from data. This makes it difficult to prove compliance, implement effective governance, or ensure that AI applications don’t inadvertently expose personally identifiable information (PII). As regulatory scrutiny on AI increases, businesses must prioritize AI-specific security policies and governance frameworks to mitigate legal and compliance risks.

4. Risk of Unintentional Data Exposure

Even without malicious intent, generative AI models can unintentionally leak sensitive or proprietary data. For instance, employees using AI tools may unknowingly input confidential information into public models, which could then become part of the model’s training data and later be disclosed through the model’s outputs. Generative AI models—especially large language models (LLMs)—are particularly susceptible to data extrapolation attacks, where adversaries manipulate prompts to extract hidden information.

Techniques like “divergence attacks” on ChatGPT can expose training data, including sensitive enterprise knowledge or personally identifiable information. The risks are real, and the pace of AI adoption makes data security awareness across the organization more critical than ever.

For further insights, explore our analysis of “Emerging Data Security Challenges in the LLM Era.”

Top 5 Strategies for Securing Your Data with AI

To integrate AI responsibly into your security posture, companies today need a proactive approach is essential. Below we outline five key strategies to maximize AI’s benefits while mitigating the risks posed by evolving threats. When implemented holistically, these strategies will empower you to leverage AI’s full potential while keeping your data secure.

1. Data Minimization, Masking, and Encryption

The most effective way to reduce risk exposure is by minimizing sensitive data usage whenever possible. Avoid storing or processing sensitive data unless absolutely necessary. Instead, use techniques like synthetic data generation and anonymization to replace sensitive values during AI training and analysis.

When sensitive data must be retained, data masking techniques—such as name substitution or data shuffling—help protect confidentiality while preserving data utility. However, if data must remain intact, end-to-end encryption is critical. Encrypt data both in transit and at rest, especially in cloud or third-party environments, to prevent unauthorized access.

2. Data Governance and Compliance with AI-SPM

Governance and compliance frameworks must evolve to account for AI-driven data processing. AI Security Posture Management (AI-SPM) tools help automate compliance monitoring and enforce governance policies across hybrid and cloud environments. 

AI-SPM tools enable:

  • Automated data lineage mapping to track how sensitive data flows through AI systems.
  • Proactive compliance monitoring to flag data access violations and regulatory risks before they become liabilities.

By integrating AI-SPM into your security program, you ensure that AI-powered workflows remain compliant, transparent, and properly governed throughout their lifecycle.

3. Secure Use of AI Cloud Tools

AI cloud tools accelerate AI adoption, but they also introduce unique security risks. Whether you’re developing custom models or leveraging pre-trained APIs, choosing trusted providers like Amazon Bedrock or Google’s Vertex AI ensures built-in security protections. 

However, third-party security is not a substitute for internal controls. To safeguard sensitive workloads, your organization should:

  • Implement strict encryption policies for all AI cloud interactions.
  • Enforce data isolation to prevent unauthorized access.
  • Regularly review vendor agreements and security guarantees to ensure compliance with internal policies.

Cloud AI tools can enhance your security posture, but always review the guarantees of your AI providers (e.g., OpenAI's security and privacy page) and regularly review vendor agreements to ensure alignment with your company’s security policies.

4. Risk Assessments and Red Team Testing

While offline assessments provide an initial security check, AI models behave differently in live environments—introducing unpredictable risks. Continuous risk assessments are critical for detecting vulnerabilities, including adversarial threats and data leakage risks.

Additionally, red team exercises simulate real-world AI attacks before threat actors can exploit weaknesses. A proactive testing cycle ensures AI models remain resilient against emerging threats.

To maintain AI security over time, adopt a continuous feedback loop—incorporating lessons learned from each assessment to strengthen your AI systems

5. Organization-Wide AI Usage Guidelines

AI security isn’t just a technical challenge—it’s an organizational imperative. To democratize AI security, companies must embed AI risk awareness across all teams.

  • Establish clear AI usage policies based on zero trust and least privilege principles.
  • Define strict guidelines for data sharing with AI platforms to prevent shadow AI risks.
  • Integrate AI security into broader cybersecurity training to educate employees on emerging AI threats.

By fostering a security-first culture, organizations can mitigate AI risks at scale and ensure that security teams, developers, and business leaders align on responsible AI practices.

Key Takeaways: Moving Towards Proactive AI Security 

AI is transforming how we manage and protect data, but it also introduces new risks that demand ongoing vigilance. By taking a proactive, security-first approach, you can stay ahead of AI-driven threats and build a resilient, future-ready AI security framework.

AI integration is no longer optional for modern enterprises—it is both inevitable and transformative. While AI offers immense potential, particularly in security applications, it also introduces significant risks, especially around data security. Organizations that fail to address these challenges proactively risk increased exposure to evolving threats, compliance failures, and operational disruptions.

By implementing strategies such as data minimization, strong governance, and secure AI adoption, organizations can mitigate these risks while leveraging AI’s full potential. A proactive security approach ensures that AI enhances—not compromises—your overall cybersecurity posture. As AI-driven threats evolve, investing in comprehensive, AI-aware security measures is not just a best practice but a competitive necessity. Sentra’s Data Security Platform provides the necessary visibility and control, integrating advanced AI security capabilities to protect sensitive data across distributed environments.

To learn how Sentra can strengthen your organization’s AI security posture with continuous discovery, automated classification, threat monitoring, and real-time remediation, request a demo today.

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