PT AF Cloud

Positive Technologies Adds LLM Firewall Module to PT Application Firewall for AI Service Protection

Positive Technologies has developed the LLM Firewall module as part of its PT Application Firewall solution to secure AI services against prompt injection attacks and data leakage. The module inspects both user requests sent to large language models and the responses generated by those models, detecting attempts to bypass restrictions, extract system instructions, or misuse agent capabilities. It specifically targets prompt injections hidden in documents or direct inputs that could override the model's original instructions. Additional detection covers personal data, passwords, access keys, and toxic or prohibited content in both directions of communication. The solution integrates via OpenAI-compatible APIs and is already available in PT AF Cloud, with deployment in PT AF PRO planned for later. Organizations can configure separate policies for different AI services, choosing between monitoring and blocking modes for requests and responses independently.

PT AF Cloud Uses Machine Learning to Detect Shifts in Web Attack Patterns from Millions of WAF Events

Positive Technologies has detailed how its PT AF Cloud service applies anomaly detection to WAF events, turning tens of millions of daily security events into actionable weekly alerts for SOC teams. The system builds traffic profiles based on the distribution of triggered protection rules for each tenant and compares current hourly or daily profiles against a weighted historical baseline using Jensen-Shannon distance. This approach identifies meaningful changes in attack composition even when overall event volume remains stable, catching the start of scans, exploitation of newly published vulnerabilities, or post-release application changes that trigger new rules. Two detection modes operate in parallel: an hourly mode for rapid spikes and a daily mode for gradual shifts distributed across multiple hours. Alerts include comparative tables of top rules, unique attack vectors, and direct links to the relevant events in the PT AF interface. Over four weeks the detector surfaced roughly 1,100 anomalies across the cloud, with expert review confirming that 69 percent indicated either an attack or a protection misconfiguration.