AI holds so much promise for resource-strapped organizations. This technology could benefit organizational functions across the board from customer service to supply chain decisions. However, as its influence grows, so do the risks of bad data, model theft, adversarial inputs, and other threats.
If your team is developing or deploying AI models, you need a security strategy that goes beyond standard IT protocols. That means protecting your data pipeline, locking down access, and defending against attacks that specifically target AI models. Follow these best practices to secure your AI model against evolving cyber threats.
AI security starts with secure, accurate data. Ensure your training data is clean, compliant, and traceable.
Implement access controls, encryption at rest and in transit, and formal review procedures to catch bias, errors, or injection attempts early. Keep detailed audit trails on who accessed, labeled, or modified training data to detect unauthorized manipulation.
How robust is your model against the latest threats? You can’t know your model’s blind spots without regular testing.
In addition to following security best practices, such as implementing firewalls, your organization also needs to consider AI red teaming. This process leverages ethical hackers to thoroughly test your model for potential weaknesses, including novel or creative threats.
Mindgard not only conducts automated red teaming through its Offensive Security for AI solution but also brings human expertise to the table, providing organizations with the best of AI-assisted testing combined with human creativity.
Who can use your model, and how? Just like any API or service, AI models should have tiered access, authentication, and throttling mechanisms in place.
This should include API keys or OAuth tokens, role-based access control, and rate limiting. Set up continuous monitoring to track usage anomalies, such as spikes in specific queries that may indicate a data extraction attempt.
Model reliability and security go hand-in-hand. To secure your AI model, you need to take a zero-trust approach. Never assume a user is safe; always validate inputs, even from established users.
Zero-trust is essential because internal teams or “trusted” users can accidentally (or intentionally) introduce bad data that causes downstream security issues. Use strict schema validation, input length and type constraints, and whitelisting or blacklisting for input formats.
AI model security largely depends on data cleanliness and protecting against adversarial inputs. However, malicious actors may also try to steal or copy your AI model.
Organizations spend countless resources producing AI models, so watermarking is a must for safeguarding your investment. Add a digital watermark or trackable fingerprint to your model outputs to ensure authenticity.
This approach is especially helpful if you're distributing models to clients or partners. While it won’t prevent theft, watermarking will give you a clear path for detecting and responding to copycats.
Some enterprise organizations may have large internal teams dedicated to securing AI models, but the majority of businesses lack the budget or headcount to do so. Fortunately, the right mix of AI-specific tools can help small data and security departments maximize their security posture.
If you don’t already have one, consider tools for AI security posture management (AI-SPM), which help you stay safe and compliant, even with multiple AI models. For example, platforms like Mindgard offer runtime protections, attack simulations, and security scoring across your AI stack.
AI models are more than just code. These decision-making engines shape customer experiences, business outcomes, and brand reputation. You invest countless hours and resources into your AI models, which is why a robust security framework is so important.
Without the proper protections, AI models become liabilities just waiting to be exploited. Integrate the protective measures in this guide to reduce your risk of attacks and data leaks while building trust with users and clients.
Don’t wait for a breach to take action. The best time to secure your AI is before deployment. Safeguard your models before attackers find their way in: Book your Mindgard demo now.
Look for anomalies in model behavior, like unexpected output patterns, increased error rates, or sudden spikes in specific query types. Tools like AI-SPM platforms can also monitor for real-time threats, unauthorized access, and unusual data flows that may indicate a breach.
Industries that rely heavily on AI for decision-making or sensitive data processing, such as finance, healthcare, defense, and eCommerce, are especially vulnerable. However, as AI adoption expands, any organization using machine learning in production should take security seriously.
Continuously. At a minimum, you should assess your model’s security posture before deployment, after major updates or retraining, and during periodic audits. Automating parts of the review with AI security tools can help you stay vigilant without compromising performance.
