From generating marketing content to automating data-driven decisions, AI improves many organizational processes. However, without the proper safeguards in place, nefarious hackers can manipulate and weaponize AI systems. Failure to protect AI models puts everything from intellectual property to user trust at risk.
That’s why every organization must adopt strong AI security best practices. Whether you’re fine-tuning a language model, deploying AI in customer-facing tools, or experimenting with generative content, the security of your models must evolve in tandem with their capabilities. Follow these foundational AI security best practices to reduce risk and build safer AI from the ground up.
One of the most effective ways to uncover AI vulnerabilities is through AI red teaming and adversarial training. These proactive techniques simulate real-world attacks to expose how models can be manipulated, misled, or compromised.
AI red teaming involves assembling internal or third-party experts who think like attackers to test AI systems for weaknesses. They conduct attack simulations to help teams understand how malicious actors might bypass filters, exfiltrate sensitive information, or trick a model into dangerous behaviors.
Adversarial training goes a step further by incorporating these attack methods into the model’s training process. By introducing edge-case inputs and intentionally corrupted data, developers can build more robust, threat-aware models that resist manipulation.
Organizations don’t need large internal teams to do red teaming, either. Security tools like Mindgard’s Offensive Security for AI are purpose-built for continuous automated red teaming (CART), which allows data teams to stress-test a model before bad actors do.
Model watermarking refers to the process of embedding identifiable patterns or signals, either visible or invisible, into AI outputs. These markers can help organizations detect unauthorized use of their proprietary models. In the age of deepfakes and synthetic media, watermarking provides a digital fingerprint that reinforces trust and transparency.
For businesses deploying large language models or image generators at scale, watermarking is crucial to monitor where and how AI-generated content appears, especially in regulated industries.
Data is the lifeblood of any AI model, but if that data is flawed, biased, or maliciously manipulated, the entire system becomes vulnerable. That’s why rigorous data validation is a cornerstone of AI security.
Before data enters a training pipeline, it should undergo thorough inspection for anomalies, outliers, and potential data poisoning. With this tactic, attackers insert misleading examples into datasets to compromise model behavior.
Left unchecked, poisoned data can subtly alter a model’s outputs, degrade accuracy, or even create backdoors that attackers exploit later.
Implementing strong access controls is essential to prevent unauthorized users from tampering with models, manipulating training data, or extracting sensitive information. Best practices for strong access control include:
Security audits are comprehensive, periodic or continuous, evaluations of both your AI pipelines and deployed models. They should include:
AI security isn't optional. It’s the foundation for every system that relies on AI. However, this isn’t a one-time task. The five best practices listed here require ongoing maintenance to build trust and resilience in your AI systems.
Threats are becoming increasingly sophisticated, and your defenses must keep pace. Mindgard’s Offensive Security for AI combines proactive testing, real-time monitoring, and risk visibility to ensure you build smart, secure systems. Book a Mindgard demo today to put AI protection on autopilot.
Poor-quality or unvetted data can introduce serious vulnerabilities, including data poisoning, hidden biases, and leakage of sensitive information. If a model accesses malicious data during training, it can manipulate behavior in subtle and dangerous ways. That’s why rigorous data validation is essential.
AI models should be audited before deployment and at regular intervals post-deployment, especially with changes to data sources, model retraining, or new use cases. Continuous monitoring should complement periodic audits to catch real-time threats.
AI red teaming specifically targets the AI vulnerabilities by simulating adversarial attacks such as prompt injection, data poisoning, and model evasion. Unlike traditional pentesting, which focuses on network and application security, AI red teaming tests the model's behavior, logic, and resilience to manipulation.
