While Cloudflare protects our outer network edge from bad HTTP payloads, it cannot see inside a valid Excel spreadsheet or OCR receipt to tell if a cell contains a hidden prompt injection aimed at our LLMs. I integrated Straiker Defend AI into our FastAPI and LangChain claims pipeline to inspect multimodal file uploads, intermediate reasoning steps, and tool calls before our Fireworks AI models execute pricing or depreciation logic. What I like best is that Defend AI inspects the full agent execution trace across text, PDFs, and audio transcripts rather than just running regex filters on a single prompt. If someone uploads a claim sheet with hidden instructions inside an 'Item Description' cell trying to bypass depreciation rules or force a maximum pre-tax replacement payout, Straiker catches the goal hijacking attempt and blocks the downstream tool call in under 300 milliseconds. I also run Straiker Ascend AI in our GitHub Actions staging pipeline to automatically red-team new model weights against multi-turn prompt injections and data exfiltration before we push updates to production.

I like Straiker’s focus on securing AI applications against emerging threats. It helps me identify prompt injection, sensitive data leakage, and malicious inputs, and gives me better visibility into AI security risks. I find the real-time threat monitoring and sensitive data protection valuable because they help identify suspicious activity, block potentially unsafe responses, and reduce the risk of exposing confidential information. Having security monitoring in one place makes it easier for me to understand vulnerabilities and respond to threats quickly. It also helps my team use AI more safely and confidently. The initial setup was relatively straightforward.

I use Straiker for security and to keep data safe. I like that it’s user friendly, easy to use, easy to integrate, and gives me a smooth experience. I value the risk detection, and the initial setup was very easy.