AI Data Security: Risks, Frameworks, and Best Practices

Today, many businesses leverage AI-powered tooling to improve business processes. However, because AI tools require internal information to return advanced insights, the use of AI tools can increase the risk of sensitive data becoming exposed or misused. These concerns apply to all uses of AI but are particularly concerning in AI security tools. Because of this, AI data security—the practice of protecting data used in AI tools and machine learning—is becoming increasingly necessary. 

To reduce risk and maintain a robust security posture across the entire software development lifecycle (SDLC), it’s important to understand how AI-based security tools function within your tech stack, where they introduce and minimize risk, and best practices for their implementation. 

How AI is used in security: AI-based security solutions

With the right guardrails and security protocols, AI-powered tools can enhance cybersecurity by automating threat detection, improving vulnerability assessments, and strengthening overall defenses. Key solutions include:

• Static application security testing (SAST) tools: AI enables SAST scanning tools to analyze source code for vulnerabilities before deployment in real time.

• Security information and event management (SIEM) systems: AI can help SIEMs detect and respond to threats in real time.

• Anomaly detection: These tools employ AI models to identify unusual patterns that may indicate cyber threats.

• Threat intelligence: Machine learning-powered solutions continuously update knowledge of evolving threats.

What are AI security risks? 

AI-driven security tools have transformed cybersecurity, but they also introduce unique risks, including:

• Data exposure: AI security tools that rely on third-party servers expose sensitive data to external parties, increasing the risk of breaches.

• Model manipulation: Attackers can exploit AI models via adversarial inputs, poisoning training data, or manipulating outputs. Some file formats for neural network weights can be hijacked to execute arbitrary and potentially malicious code. 

• Bias and inaccuracy: Poorly trained AI models, or those that have not been trained on use-case-specific data, can generate incorrect or biased results, leading to flawed security decisions.

• Interoperability challenges: Some security tools cannot be integrated fully into an organization’s tech stack, increasing the risk of security gaps.

AI security best practices

To mitigate these security risks of AI, organizations need to implement best practices to ensure their proprietary data, sensitive information, and organization is protected. To start organizations can:

• Opt for curated LLMs: To improve accuracy, use purpose-built LLMs designed for niche use, with subject matter expert review if possible. 

• Use tools that host their own LLMs: Prevent data exposure by opting for tools that keep data on-premises. Otherwise, your data and your customer’s data will be sent to third-party servers for processing, increasing the risk of a data security breach. 

• Restrict data retention: Ensure customer data is only retained for the minimum necessary period and anonymized when possible to enhance security.

• Employ rigorous testing: SAST helps detect and remediate vulnerabilities early.

• Adopt a secure SDLC: Embedding security across all development stages enhances protection.

• Utilize enterprise licenses: Proprietary data remains safeguarded by leveraging enterprise agreements that prevent AI model training on user data.

How to build an effective AI data security strategy

Developing an effective AI data security strategy involves identifying potential risks, implementing safeguards, and continuously monitoring security measures. Organizations can protect their data while optimizing security processes by incorporating best practices and choosing the right AI-powered security tools: 

• Consider your options for hosting carefully: Factoring in the data handling risks involved and encryption capabilities available. 

• Be selective with file formats: When executing neural networks on your own hardware, stick to weights that are shared in the safetensors file format to avoid inadvertently executing malicious code.  

• Double-check AI’s work: Scan AI-generated code using an industry-leading SAST tool like Snyk Code. 

Steps to minimize risks

Minimizing data security risks begins with the security tool you select and how it’s implemented. Evaluate each vendor’s strategy and certifications carefully, and don’t be afraid to ask questions about model security and policies. Leading vendors like Snyk openly share the technical considerations that factored into their models’ design and operations and what they themselves are doing to minimize customers’ risk. 

Before you settle on a tool, other features to look for include:

• Smooth integration with the rest of your tooling. 

• Curated databases that power security-specific, self-hosted LLMs. 

• Clear governance and data security policies. 

• Continual maintenance and monitoring of the model to ensure performance.

Best AI-based tools for data security

When choosing a security tool, look for vendors with strong AI governance practices, security-specific expertise, and AI transparency. An added bonus: a SAST with platform support ensures security coverage across the full SDLC. 

Leading vulnerability scanning tools build on deep knowledge of the obstacles faced by security teams and developers alike. For instance, Snyk Code uses DeepCode AI to power autofixes with over 80% accuracy during SAST, making vulnerability remediation fast and easy to accomplish early in the development lifecycle. Snyk Code’s self-hosted LLM reduces the risk of data leaks or inaccuracies.

To learn more about enacting stronger security in AI implementations, read our SAST/SCA buyer’s guide for the AI era.