We’ve recently seen a spike in malware that uses LLMs to rewrite its own source code on the fly to evade signature-based detection. It’s becoming a cat-and-mouse game that my current EDR struggle to keep up with. Is anyone here successfully using "AI-native" security platforms like Darktrace or SentinelOne to counter these automated threats? What are the best practices for setting up behavioral triggers that don't result in a flood of false positives?
3 answers
The shift to AI-driven threats has forced us to move entirely away from static signatures. In my current role at a mid-sized fintech, we’ve pivoted toward a "Zero Trust" architecture combined with an AI-driven XDR. We found that the key to managing false positives is to feed the AI a "clean" baseline of our network behavior for at least three months. Now, instead of flagging every new script, the system only alerts us when a process attempts to exfiltrate data to an unknown IP or starts encrypting local files in an unusual pattern. It’s not perfect, but it caught two major "Vibe-coded" scripts last month.
Linda, that baseline strategy sounds solid, but how do you handle "legitimate" changes in dev behavior that might look like an anomaly to the AI?
We are focusing on "Prompt Injection" security. Many of our internal AI tools are vulnerable to being tricked into leaking system credentials if not properly sanitized.
Prompt injection is the new SQL injection. Every developer needs to be aware of how to sanitize inputs for LLMs just as much as they do for traditional databases.
Steven, we actually integrated our Jira tickets with our security alerts. If a developer is working on a high-intensity deployment, the system cross-references the ticket and lowers the "anomaly score" for that specific machine during the window. This drastically reduced our false positive rate by about 40% and kept our SOC team from burning out during big releases.