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Dec 9, 2025
Indusface Introduces AppTrana AI Shield to Help Organizations Safely Scale GenAI Across the Business
Indusface Introduces AppTrana AI Shield to Help Organizations Safely Scale GenAI Across the Business
@businessline
New AI firewall, delivered as a fully managed service, protects chatbots, copilots, and other LLM-powered apps from misuse and data exposure, so security and compliance teams can support GenAI adoption without slowing down the business.
BENGALURU, India, Dec. 4, 2025 /PRNewswire/ -- Indusface, a global web and API security provider, today launched AppTrana AI Shield at DSCI AISS 2025. The new AI firewall is designed to protect AI and GenAI applications, including customer-facing chatbots and internal copilots, from sensitive data leakage, fraud, misuse of AI-generated responses, and other emerging cyber threats. It enables organisations to adopt GenAI without compromising on 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 consisten
