Agentic App for real-time AI security management
Client: A US-based deep-tech cybersecurity startup focused on securing AI systems.
Industry: Cybersecurity, AI Security
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Client
Client
Operating at the intersection of cybersecurity and AI, a US-based startup addressing the critical security gaps introduced by the rapid adoption of Large Language Models (LLMs).
Challenge
Challenge
- The product required building an AI system capable of monitoring and policing other AIs in real time
- Threat actors were beginning to target AI models directly through prompt injection, model poisoning, and malicious agent workflows
- Existing security tools could secure the perimeter while remaining blind to AI-layer data exfiltration risks.
Solution
Solution
- 4-week AI-first Sprint Zero de-risked MVP scope, threat models, and architecture before development
- Compliance controls embedded into workflows to minimize friction
- CISO-grade command center translating detection signals into clear enterprise risk posture reports
- Java + Python ML detection engine identified prompt injection, poisoning, and malicious agents
- Java, React, Python, Mongo DB, AWS Bedrock
Impact
Impact
- Delivered a prototype in 6 weeks and a first-to-market AI security platform in 12 weeks
- Covered 22,000 agentic user flows across the platform
- Reduced compliance review time by 40% for enterprise design partners
- Achieved 50% faster detection-to-remediation cycles, the metric CISOs use to justify security investments
- Secured $2M+ in seed funding on the back of the MVP, validating both the product and the market
Strategic Significance
Strategic Significance
Demonstrates Indexnine’s ability to turn emerging AI security risks into a real-time CISO command center, combining speed, technical depth, and enterprise-grade UX to simplify management of complex risk signals.
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