Indusface, a global web and API security provider, launched AppTrana AI Shield, an AI firewall designed to protect AI and GenAI applications such as customer chatbots and internal copilots from sensitive data leakage, fraud, misuse of AI-generated responses, and other emerging cyber threats. It helps organizations adopt GenAI without compromising security, compliance, or brand reputation.

As organizations embed LLMs into customer support, analytics, employee copilots, and knowledge search, they expose a new attack surface that traditional WAFs, and API gateways do not fully address.

"Boards and leadership teams see GenAI as a way to transform how they serve customers and employees, but if these systems are misused, they can leak sensitive data such as PII, fuel fraud, and invite regulatory scrutiny," said Ashish Tandon, Founder and CEO, Indusface. "With AppTrana AI Shield, organizations can roll out AI features faster, while our team focuses on keeping sensitive data and critical workflows safe."

Exposure of sensitive data from knowledge bases, retrieval abuse, prompt injection, and automated LLM probing by bots are now top concerns for security and compliance leaders.

AppTrana AI Shield is built to address this gap by helping organizations:

• Prevent sensitive data leaks from AI use cases by enforcing policies mapped to OWASP LLM Top 10 and giving security teams clear, audit-ready visibility.
• Apply centralized control and guardrails across AI endpoints by inspecting every prompt and response inline, blocking policy-violating queries and filtering out unapproved or high-risk outputs before delivery.
• Adopt any LLM behind a consistent, model-agnostic security layer that protects public, hosted, or private models via existing AppTrana API onboarding workflows.

Managed AI security with integrated bot protection

AppTrana AI Shield combines AI-specific checks, integrated bot protection, and a fully managed service so organizations don't have to build an AI security team from scratch. It detects and mitigates automated prompt storms, brute-force attempts, and scraping of AI-powered interfaces, and applies different rate limits and access controls for real users, internal tools, partners, and hostile automation. Indusface experts design and tune policies, monitor LLM traffic 24x7 for abuse and anomalies, and work with customer teams to refine guardrails and reduce false positives, backed by audit-ready reports that show how AI and OWASP LLM Top 10 risks are controlled.