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Enhancing Data Security: A Crucial Shift in the Generative AI Era

data security

data security

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise software categories. Don Arden of Fasoo examines how the popularity of Generative AI has pushed for a crucial shift in data security.

When OpenAI introduced ChatGPT, the world of content creation shifted dramatically.  Amazon, Google, Microsoft, and many other companies launched generative AI tools based on large language models (LLM) to create various types of content, including images, designs, code, emails, movie scripts, and marketing materials.

Artificial intelligence has the potential to revolutionize business, but it poses significant risks to your data.  Two main risk categories are content anomaly detection and data protection.  They include loss of intellectual property, privacy concerns, lack of transparency, bias, discrimination, and inaccurate or unwanted responses to inputs.

Sometimes, the AI produces completely false information.  This is known as AI hallucination.  The dangers of AI hallucinations include legal liability, compliance risks, and real-world consequences.  In one case, an AI fabricated case law that attorneys presented in court.  Most of us have experiences where the answers to our prompts aren’t quite what we expected.  In some cases, it’s inconvenient.  In other cases, it’s catastrophic to your business.

Misuse of AI can lead to major privacy and security issues since the models collect and process vast amounts of data.  As users access these tools to generate content, they enter data so the AI can learn and provide better responses in the future.  This process is prone to data leakage and compromising confidentiality since you have little to no control over these hosted environments.  Users could mishandle information by adding proprietary or regulated data to the prompts, resulting in a data breach, intellectual property theft, and other forms of abuse.

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Enhancing Data Security: A Crucial Shift in the Generative AI Era

Enhancing Data Security to Minimize Risks

Your inability to conduct a privacy impact assessment or implement your own data protection policies on these systems may lead you to block access to all AI services.  There are many legitimate uses for generative AI to improve revenue and grow your business, so you must take advantage of them.  Your competitors already are, so you can’t be left behind.  Also, it’s not feasible to stop users from accessing these tools anyway.  People will always find a way to access something of value.

Using AI will help increase your competitive advantage, but you also need to mitigate risks from misinformation, sharing personal and proprietary data, and other vulnerabilities on employees and contractors.  If sensitive third-party or internal company information is entered into a public service, like ChatGPT or Bard, that information will become part of the chatbot’s data model and can be shared with others who ask relevant questions, resulting in data leakage.  Any unauthorized disclosure of confidential information may violate your organization’s security policies or privacy laws like GDPR, HIPAA, or CCPA.

Anyone using these services should treat the information they input as if they were posting it on a public site, like Instagram or LinkedIn.  They should not post personally identifiable information (PII)– company or client information that is not generally available to the public.  There are currently no clear assurances of privacy or confidentiality in these systems, so you need to guard against someone inadvertently copying and pasting customer or proprietary data into the prompt.  The information you post will be used to train the model further and will become the answer to someone else’s question.

Context-based Discovery

Data submitted to generative AI models can result in data compromise if sent to environments that are not adequately secured and protected.  The first step to improved security is to discover sensitive data in existing files on servers, in the cloud, or endpoint devices using machine learning to understand the content and context of the information.  If an employee or contractor generates a document utilizing an LLM that contains sensitive data, you can automatically identify it.

After identifying sensitive data, you should immediately classify and add a label to files, quarantine, or assign adaptive access control to authorized users.  Once identified, it’s easy to categorize obsolete, redundant, and sensitive data.  Remediation should be automatic based on configurable rules which prevent violating privacy or other security standards.

Advanced Data Protection

By automatically encrypting and assigning dynamic access control to sensitive files, you can limit editing, copying, printing, screenshots, and general sharing of sensitive content with unauthorized users and systems both inside and outside your organization.  You ensure that only authorized users can access your sensitive data based on security policies that validate user access continuously.

This prevents users from copying and uploading sensitive data to ChatGPT and other generative AI models and protects your organization from insider threats and external attacks.  If you train an internal LLM by crawling your data stores, it won’t ingest any encrypted files, since it can’t read them.  By default, that eliminates sensitive data from compromising your model.

Intelligent Monitoring

Tracking file access and the usage of sensitive data prevents information leaks by protecting and controlling the data before it gets into the wrong hands.  You can easily monitor usage patterns to understand who is accessing IP, regulated data, and other proprietary information, regardless of location.

Implementing dynamic file usage policies through centralized policy management can accommodate changing business requirements.  Requiring user validation each time they access a file, ensures that changed policies are implemented in real-time.  This allows you to grant file access to those who need it when they need it.  You can also remove access privileges immediately to address any potential data compromise.

Redefining Your Data Security Strategy

Discovering sensitive data, encrypting it, assigning explicit access controls, and using intelligent monitoring to prevent information leaks, helps protect your sensitive IP and regulated data.  Identifying and protecting your sensitive data as you create it is the best approach to control its access.  This helps restrict what users upload to public or private generative AI services to minimize your risk of violating privacy regulations or compromising your business.  If you download something sensitive as a result of using AI, the same approach flags it as sensitive so you can mitigate privacy and security violations.

These initiatives empower you to enhance your data security by identifying potential risks and vulnerabilities, implementing robust security controls, and ensuring continuous data visibility throughout its entire lifecycle.  These elements are essential pillars of a robust data security strategy, providing invaluable support as you navigate the evolving landscape of AI.

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