Traditional Data Loss Prevention (DLP) systems are insufficient for securing generative AI and agentic applications. While DLP focuses on identifying known sensitive content within established channels like email, files, and removable media, it fails to analyze the semantic meaning of AI prompts or responses.
LLM and AI Firewalls operate differently by intercepting traffic between users and AI models. They evaluate requests and outputs in real-time, checking for jailbreaks, prompt injections, and unauthorized tool calls. Unlike DLP, these systems manage model access, token consumption, and routing policies before data is processed.
The rise of Shadow AI, where employees use unauthorized AI tools or bypass corporate gateways, creates significant security gaps that standard DLP cannot address. Specialized tools like INFERA AI.SafeAgent are required to monitor AI agents on workstations and prevent unauthorized actions before they are executed.
The security risks associated with AI are categorized by frameworks such as OWASP Top 10 for LLM and Agentic Applications, as well as MITRE ATLAS. These include threats like prompt injection, data leakage, and malicious tool usage, which require dedicated controls rather than traditional signature-based filtering.
Effective corporate security requires a layered approach where DLP handles traditional data channels while AI Firewalls and agent runtime security manage the unique risks of generative models. This strategy ensures proper audit trails, budget control for token usage, and strict enforcement of access policies.
