GenAI is Transforming Data Security: What Security Leaders Need to Know

The rise of Generative AI (GenAI) is reshaping data security programs in profound ways, requiring your security team to rethink traditional approaches. GenAI Tools like ChatGPT or DeepSeek have become incredible tools for Product teams, Marketing teams, and Sales reps. Which means that you probably have more employees using these tools, and inputting company data in them, than you realize. But don't worry, you're certainly not the only team dealing with this, and our advice is to lean in, but do so with a data-led approach.

As your team rushes to capitalize on GenAI’s potential, there are some new challenges that emerge around data protection, privacy, and compliance that you should be aware of. This article explores three monumental shifts that are driving these changes:

1. The Urgent Need for AI-Native Data Security Platforms for GenAI

As organizations integrate GenAI into their workflows, a new risk emerges: data exposure through AI model training, and through prompts. If not properly governed, GenAI can inadvertently expose sensitive data to unauthorized internal users, external vendors or cloud providers, and even create internal access risks through careless prompting.

To mitigate these risks, organizations must prioritize Data security solutions that were designed with AI-native architectures solutions, which:

• Discover and Classify Data: Identify sensitive data across structured and unstructured repositories with speed, scale, and hyper-precision. Pro-Tip - During your bake-off, prioritize deployment speeds, scan speeds, and coverage. Any solutions that offer less than 95% precision classification should be avoided. AI-native architectures should be a prerequisite for any data security platform you select.
• Identify Gen AI tools without over-privileged access: Identify, and view context around the non-human identity, and determine what sensitive data is accessible to these tools. Then looks to minimize access where unnecessary to reduce risks
• Monitor GenAI Access: Track how AI models interact with data, ensuring compliance with security policies.
• Detect Data Pipelines: Uncover unauthorized connections to external systems and mitigate third-party risks. Prevent sensitive data from leaking into GenAI tools, and unsanitized data from being fed into LLMs

Begin with adopting a DSPM solution which focuses primarily on Discover and Classify. This is the foundation component of all data security. These solutions have evolved from a niche concept to a mainstream security practice, security leaders must adopt these capabilities as part of their broader Data Security strategy.

2. Synthetic Data Over Traditional Anonymization in GenAI Training

Through our conversations with our Fortune 2,000 customers, we see that data teams are increasingly favoring synthetic data over traditional anonymization techniques for training AI models. Mature enterprises looking to mask sensitive data now leverage solutions that generate industry-specific or custom synthetic datasets - helping to reduce risks of real data being exposed. Unlike anonymized data, synthetic data ensures stronger privacy preservation while addressing the common challenge of insufficient real-world training data.

Here are some of the advantages of this approach:

• Enhanced Data Privacy and Data Protection: By generating artificial datasets, organizations minimize risks associated with using real-world data, which often contains sensitive information.
• Improved AI Model Performance: Synthetic data enables training on diverse edge cases and rare scenarios, improving AI reliability.
• Bias and Error Management: AI-generated synthetic data can be supervised to prevent reinforcing biases or, conversely, to introduce intentional bias for specific use cases like fraud detection.

For industries like healthcare and finance, where privacy regulations are more stringent, synthetic data is becoming an essential component of AI adoption. Real data is only used periodically to validate model alignment and monitor for drift, reducing exposure to sensitive information. So use synthetic data!

3. The Shift from Structured to Unstructured Data Security

Historically, data security solutions focused on structured data, such as databases and transactional records. However, GenAI’s capabilities extend far beyond structured data—because they have the ability to process vast amounts of unstructured data including text, images, and videos. The hard stuff.

Like the two megashifts we already discussed, this shift also has major implications for security teams:

• Unstructured Data as a Security Priority: Organizations are becoming increasingly aware of the value—and risk—associated with unstructured data. 
• Broader Attack Surfaces: Sensitive information often resides in emails, chat logs, legal documents, and media files, all of which GenAI can process. We have seen examples with admins for executives who have a special folder which contains credit card numbers, SSNs, and bank accounts. So this is much more common than people realize.
• New Protection Strategies: Traditional database security solutions are insufficient. Enterprises must now deploy security measures that encompass the full spectrum of data formats.

With GenAI democratizing access to unstructured data analysis, security teams must ensure that the proper safeguards are in place to prevent unauthorized access and leakage. This is not always easy given the speed in which GenAI is being adopted, and the fact that security never wants to be seen as the “Department of no.”

Wrapping things up…

The demand for GenAI continues to grow, with enterprises eager to harness AI’s potential across various applications. However, concerns over data accuracy, privacy, and regulatory compliance remain significant barriers to adoption. The EU AI Act is a prime example of this rapidly evolving AI space.

Senior executives are now pressuring IT and security leaders to address these challenges head-on. As a result, budgets are being allocated toward a mix of mature security controls and innovative early-stage technologies, particularly in the DSPM space. In fact, most of the CISO and CIOs we speak with often claim that their budgets are being reduced in every area EXCEPT data security. 

Some strategic actions for you Data Security leaders out there

1. Invest in DSPM Technologies – Implement tools that provide visibility into GenAI data access and ensure alignment with security policies.Adopt Synthetic Data Generation – Shift from traditional anonymization to synthetic data solutions to reduce privacy risks and compliance burdens.
2. Enhance Unstructured Data Protection – Deploy security measures that govern text, images, videos, and other unstructured data types increasingly used by GenAI.
3. Allocate Budgets Strategically – Make no mistake, your company wants to adopt AI. So, shift resources to data security to a more holistic GenAI security approach that will help you safely enable AI adoption, without putting your data at risk. This meme comes to mind

GenAI presents both tremendous opportunities and new risks for enterprises. By proactively addressing AI data security concerns, the smart organizations will unlock the benefits of AI while safeguarding sensitive information. 

Act now to take advantage of this AI-driven era.

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