Data security was already an increasing concern for cyber security teams and organizations, but the growth of generative AI (GenAI) has substantially increased the stakes. In fact, CSO reports that nearly 10% of GenAI prompts initiated by employees contain sensitive data, and a staggering 97% of organizations using GenAI have experienced security breaches linked to its use.
Many GenAI deployments now move faster than their own risk controls. From model training to API outputs, sensitive data can slip through the cracks if security isn't baked in from the start.
While many factors contribute to GenAI breaches, strong data security is the foundation for the responsible and safe use of AI. Follow these best practices to secure your GenAI systems and stay ahead of threats without slowing innovation.
Before implementing GenAI-specific protections, make sure your other data protections are robust and airtight. Protecting your system starts with the basics: secure the physical and cloud infrastructure that stores and processes your data. That includes everything from data centers and network architecture to third-party software dependencies.
To minimize exposure, implement network segmentation, deploy robust endpoint protection, and maintain an ongoing vulnerability management program. These practices reduce your attack surface and limit the potential damage of a breach.
GenAI models can unintentionally expose sensitive information, which is why data loss prevention (DLP) is so important. Use AI security tools with DLP capabilities to detect, monitor, and restrict the flow of confidential or regulated data both during model training and while in production.
The goal is to prevent the model from exposing sensitive information—whether through training data leakage, overfitting, or unsafe prompt outputs. Effective DLP for GenAI should include safeguards to detect and redact personally identifiable information (PII), protected health information (PHI), financial data, and intellectual property from both training inputs and real-time interactions. It should also monitor model behavior to identify and block risky or non-compliant outputs.
What goes in and comes out of a GenAI model can both become attack vectors. That’s why it’s essential to treat every prompt and output as untrusted by default. Start by validating and sanitizing all inputs to guard against prompt injection, jailbreak attempts, and other adversarial attacks.
Rather than relying on a single method, use layered input validation that combines rule-based filters (e.g., regular expressions, keyword blacklists) with AI-driven anomaly detection to identify obfuscated threats.
Whether you're training models, running inferences, or handling user inputs, encryption is non-negotiable. Apply AES-256 or equivalent encryption to all data—at rest, in transit, and during processing.
This includes training datasets, inference requests, model outputs, and system logs. Don’t rely on default configurations; enforce end-to-end encryption across every layer of your GenAI pipeline.
For high-sensitivity environments, explore advanced approaches like confidential computing and homomorphic encryption. While homomorphic encryption enables computations on encrypted data without exposing plaintext, it remains resource-intensive and best suited for specific, high-risk workflows. In most cases, confidential computing using secure enclaves offers a more practical balance between performance and data protection.
Users shouldn’t have unfettered access to your systems. While role-based access control (RBAC) is a best practice for all infrastructure, it ensures that only authorized users can access a GenAI model’s most sensitive data.
Define granular roles and permissions for every user, service, and system interacting with your GenAI stack. Limit access to only what’s necessary, whether that’s for prompt inputs, model training tools, or API outputs.
Even the best defenses can fall short if they aren’t regularly tested. GenAI systems evolve quickly, and so do the threats that target them. That’s why continuous security validation is essential. Offensive testing techniques, such as red teaming, help identify vulnerabilities before attackers do, but traditional red teaming wasn’t designed for AI systems.
That’s where AI-specific solutions, such as continuous automated red-teaming (CART) tools, come in. CART simulates real-world adversarial attacks against your models, APIs, and pipelines to uncover hidden risks, such as prompt injection, data leakage, model inversion, or unexpected behavior under adversarial inputs.
By proactively identifying and remediating vulnerabilities on a continuous basis, you can stay ahead of emerging threats and reduce risk across the full AI lifecycle.
Generative AI helps businesses do more in less time, but this technology isn’t without risk. The majority of organizations already using GenAI have experienced security incidents, and with sensitive data on the line, you can’t afford to rely on legacy protections alone.
Follow the best practices in this guide to set up your GenAI models for success. However, this is just the baseline. GenAI needs constant testing and monitoring, which is why Mindgard’s Offensive Security platform is such a game-changer. Protect your data and models: Book your free Mindgard demo today.
It can, but for most use cases, the impact is minimal. Standard methods like TLS 1.3 and hardware-accelerated AES-256 are highly efficient and typically introduce negligible latency. However, advanced techniques like homomorphic encryption can significantly affect performance and are best reserved for high-sensitivity, niche use cases where privacy outweighs speed.
Yes, and that’s one of the biggest risks of GenAI. If you include sensitive information in training data without proper safeguards, such as masking, encryption, or differential privacy, the model may inadvertently generate it in future outputs. That’s why input hygiene matters just as much as output filtering.
No. While firewalls and endpoint protection still matter, GenAI introduces new attack vectors that require AI-specific defenses. Threats like prompt injection and model exploitation target the unique behaviors of AI models, demanding continuous testing, monitoring, and safeguards beyond standard security protocols.
