Indusface announced 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 organisations adopt GenAI without compromising security, compliance, or brand reputation.
As organisations 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, organisations 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 organisations:
- Prevent sensitive data leaks from AI use cases
Policies and controls are organised around OWASP LLM Top 10 risks, including prompt injection, data exfiltration and sensitive information disclosure. This gives security and compliance teams a clear way to reason about coverage, reduce data exposure, and report AI risk. - Apply centralised control and guardrails across all AI endpoints
Placed inline with requests to AI agents, AppTrana AI Shield inspects every inbound prompt and outbound response. Malicious or policy-violating prompts can be blocked or adjusted before they reach the model, and responses are checked for sensitive information or unapproved responses before they are returned to users. - Adopt any LLM behind a consistent, model-agnostic security layer
The solution protects LLMs and AI services accessed over APIs, whether they are public models, hosted models, or private deployments in cloud or on premises. It reuses existing AppTrana API onboarding workflows, so teams can protect AI endpoints without changing application code or redesigning their architecture.
Integrated bot protection for LLM abuse
Uncontrolled automation can flood AI assistants with prompts, drive up usage costs, and scrape high-value outputs. The AI firewall is tightly integrated with Indusface’s bot protection module to prevent AI misuse at scale. This allows organisations to:
- Detect and mitigate automated prompt storms, brute force attempts, and scraping of AI-powered interfaces.
- Distinguish between real users, internal tools, partner applications, and hostile automation.
- Apply differentiated rate limits, access controls, and guardrails based on identity and risk.
By combining AI-specific checks with AI-powered bot management, enterprises can reduce abuse of AI features, keep costs and performance predictable, and maintain a smooth experience for legitimate users.
“AI attacks are rarely one-off. They are automated, iterative, and noisy,” said Ashish. “We combine LLM-aware policies, bot detection, and a fully managed service so customers can spot and stop these patterns early, without building their own AI security team or constantly interpreting new risks.”
Fully managed AI security beyond rule packs
Like the rest of the Indusface platform, AppTrana AI Shield is delivered as a fully managed service, so enterprises are not left to figure out AI security on their own.
Indusface security experts:
- Design and tune AI firewall policies based on each customer’s use cases, data sensitivity, and risk appetite.
- Monitor LLM traffic 24×7 for prompt abuse, anomalies, and emerging techniques.
- Work with customer teams to investigate incidents and refine guardrails to reduce false positives.
- Provide audit-ready reports that show how OWASP LLM Top 10 risks are identified and controlled.
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