By Collin Miller, Structured Cloud Security Director —

AI security is more than just cybersecurity. Today’s AI models are not like traditional software systems—they learn, evolve, and adapt. But this flexibility also makes them uniquely vulnerable to adversarial attacks.

The MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework helps security teams understand how AI can be exploited and what defenses are needed.

Here’s a look at some of the top AI threats—and how to stop them.

Threat 1: Prompt Injection Attacks

🔍 Example: The “DAN” Jailbreak in ChatGPT

• Hackers manipulated ChatGPT by tricking it into ignoring safety filters.
• Carefully crafted prompts bypassed ethical restrictions, allowing the AI to generate harmful content, exploit vulnerabilities, or leak sensitive data.

🔐 How to Mitigate:
✔️ Input filtering & AI firewalls – Use AI firewalls to detect malicious prompts before they reach the model.
✔️ AI Red Teaming – Deploy security researchers to simulate attacks on AI systems before real attackers do.
✔️ Multi-layered content validation – AI should double-check its outputs before displaying them to users.

Threat 2: Data Poisoning Attacks

🔍 Example: Amazon’s AI Bot Detection Failure

• Attackers poisoned Amazon’s fake review detection AI by injecting thousands of fake but realistic reviews.
• The AI learned the wrong patterns, making it ineffective at detecting real fraudulent reviews.

🔐 How to Mitigate:
✔️ Dataset integrity validation – Use cryptographic hashing (SHA-256) to ensure training data isn’t altered.
✔️ AI training on adversarial prompts – Continuously fine-tune AI to recognize and reject deceptive inputs.
✔️ Federated learning – Train AI models in decentralized environments to prevent a single poisoned dataset from compromising an entire system.

Threat 3: Model Inversion Attacks

🔍 Example: Extracting Faces from Biometric AI

• Researchers used side-channel queries to reconstruct faces from AI-powered facial recognition models.
• Attackers queried the system thousands of times, extracting data about individual identities.

🔐 How to Mitigate:
✔️ Differential privacy – AI models should add noise to training data to prevent reconstruction attacks.
✔️ Rate limiting & anomaly detection – AI APIs should detect excessive queries from a single user.
✔️ Synthetic data training – Train AI on fake but realistic data, reducing exposure of real sensitive information.

Final Thoughts: Protecting AI Systems Requires a New Approach

Traditional cybersecurity isn’t enough— Protecting AI Systems requires specialized defenses:
✅ AI-Specific Threat Models (MITRE ATLAS) – Security teams must map AI risks the way they do for traditional threats.
✅ AI Red Teaming & Stress Testing – Simulate adversarial attacks before hackers do it for real.
✅ Data Protection by Design – AI models must be hardened against manipulation, poisoning, and leaks.

If Security isn’t built into your AI program, it’s not just an Artificial Intelligence — it’s an Attack Interface.

Need help shoring up AI security? Contact your account manager or email @info@structured.com today.

About the Author

Collin Miller has more than 20 years’ experience designing secure and sustainable IT infrastructures that protect data, users, and organizational resources. As cloud computing gained traction, Collin dedicated himself to the practice of cloud security. His expertise extends to securing data and workloads in all the large public cloud providers, as well as the best practices, platforms and tools required to secure SaaS applications and the data traversing them.

With a strong background in cybersecurity, Collin brings a disciplined approach and deep knowledge of zero trust practices and secure access service edge (SASE) architectures to cloud environments. He is adept with cloud security posture management (CSPM), cloud native application protection platforms (CNAPP), cloud access security broker (CASB) platforms, and more.