Securing the AI Frontier: Treat AI Like the New Attack Surface

AI is becoming the backbone of modern business — customer support, marketing analytics, code generation, security tooling. But while companies rush to adopt it, most are ignoring a critical reality: AI is now one of the largest new attack surfaces in modern infrastructure. Attackers know it. Security teams are just catching up. That gap is where breaches happen.

AI is growing faster than security can keep up

Organizations are integrating AI systems without the controls they apply to traditional infrastructure. AI introduces risks traditional tools weren't designed to detect: prompt injection, model manipulation and data poisoning, sensitive data leakage through prompts, AI-powered social engineering, and unauthorized model access or API abuse. Often attackers don't even exploit the model — they exploit how people interact with it.

Prompt injection and data poisoning, in brief

Prompt injection is being compared to the early days of SQL injection: a malicious input tricks a model into ignoring its original instructions and exposing confidential data. Data poisoning lets attackers influence model behavior by manipulating training data — even small amounts of poisoned data can significantly alter outcomes. We cover both in depth, with a 10-point checklist you can run today, in our companion piece: When Your AI Turns Against You. Here, we focus on the two risks that piece doesn't — Shadow AI and red-teaming AI itself.

The hidden risk: Shadow AI

The biggest risk often isn't the AI you deploy — it's the AI your employees use without permission. Security teams increasingly encounter Shadow AI, where staff paste sensitive data into tools like ChatGPT, AI code assistants, document summarizers, and analytics platforms. If those prompts contain customer data, internal documents, proprietary code, or financial records, that data may leave your security boundary — and most companies have no visibility into it. You can't protect what you can't see.

AI is also supercharging cybercriminals

While defenders experiment, attackers already use AI at scale to generate convincing phishing, clone voices for social engineering, write malware faster, automate reconnaissance and scanning, and produce deepfakes. The barrier to entry for cybercrime is dropping fast — what used to require skilled hackers can now be done with a few prompts.

AI security requires a new mindset

Traditional models assume systems behave predictably. AI systems generate responses dynamically based on data, training, and prompts. That means rethinking controls: monitor AI model access and usage, protect training-data integrity, detect prompt-injection attempts, prevent data leakage through interactions, monitor the APIs used to reach models, and apply runtime protection to AI workloads.

Offensive security is the best defense

The best way to secure AI systems is to attack them first. Organizations need people who understand adversary tactics, AI exploitation techniques, prompt-injection testing, API-abuse testing, and data-exfiltration paths. Red-teaming AI systems will become just as important as traditional penetration testing — because if your team isn't testing your AI, someone else will. The organizations that win the next era of security are the ones that secure AI before attackers weaponize it at scale.

Has anyone red-teamed your AI yet? If your team isn't testing your AI systems, someone else will. We bring adversary tradecraft to prompt injection, API abuse, and Shadow AI.