Traditional security methods fall short of controlling the unique threats that AI systems face in production. You cannot rely on firewalls and Identity and Access Management (IAM) configurations to keep attackers out and retain control of your systems.
Threats Are Evolving
New methods of attack are emerging all the time, and the rate of appearance of new threats is only increasing. ChatGPT no longer falls for jailbreaks like writing poems about harmful topics or leaking data through polite requests. Today's attackers are sophisticated and determined, leveraging data poisoning, automated detection of malconfigured RAG pipelines, and phenomenal patience to find issues your engineers have missed.
Classification of Harmful Inputs
Still, most assaults on an AI system start with a simple prompt. Requests which play on model biases, quirks of post-training, or even temporarily-available vulnerabilities in model refusal are commonplace, and impossible to detect without continual monitoring of the attack surface. Detecting these threats effectively requires wide visibility, much like how Cloudflare needs its high coverage over enterprise web deployments to identify DDoS and botnet activity at scale.
How We Leverage Scale
While many attempts at manipulating deployed AI systems are blunt and barely trying to be concealed, more determined and sophisticated actors can leverage threats almost no system can detect. Data poisoning attacks can be executed over months or even years across thousands of accounts, introducing biases in your models which drift from barely perceptible to a liability risk. Through our broad coverage of customer-facing enterprise deployment of AI, we can catch these attacks in their early stages and filter these harmful inputs before they cause irreparable harm to the reputation of our clients.