Securing Enterprise AI Against Jailbreaking Risks
A customer needed to test AI models for resilience against prompt injection, manipulation, and jailbreaking attacks before allowing them into production. They wanted to evaluate multiple large language model (LLM) vendors in a secure, controlled environment - measuring response integrity, attack resistance, and mitigation performance without exposing live systems or sensitive data.
Outcomes
90%
Reduction in Monitoring Costs
98%
Attacks Prevented
92%
Customer Questions Answered
0%
Production Data Exposured
Technology Vendors Suited to Evaluation
Business Problem
As generative AI became embedded across customer and employee workflows, the bank faced rising security and compliance risks from model manipulation and prompt injection. Existing governance frameworks and controls weren’t designed for LLM-specific threats, leaving gaps that could expose sensitive data, trigger harmful outputs, or compromise intellectual property.
The bank needed a secure way to test and validate AI models against these emerging vulnerabilities before enterprise deployment.
Challenges
- Prompt Injection Vulnerabilities: Malicious prompts cause unintended/harmful outputs.
- Data Leaks & Compliance Risks: Breaches lead to regulatory penalties & reputational damage.
- Intellectual Property Theft: Unprotected models are at risk of extraction & misuse.
- Operational Inefficiencies: AI security gaps demand constant monitoring & manual intervention.
From Idea to Evidence with NayaOne
A secure, disconnected sandbox allowed for safe testing of the solutions without needing to onboard AI models and the vendors allowing the PoC to begin in weeks instead of days.
- LLMs were deployed in the sandbox with access to synthetic customer data
- The vendor tools were added ‘in front’ of the LLM into the sandbox to monitor for jailbreaking in real-time.
- Simulated ‘attack’ queries ensured that no personally identifiable information was exposed in AI outputs.
Impact Metrics
PoC Timeline Reduction
4 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The bank gained hard evidence on detection accuracy, speed, and integration fit - enabling a data-driven vendor choice and faster approval across risk and procurement.
KPIs
- Jailbreak Detection Rate (%): Percentage of successful adversarial prompts identified and mitigated.
- Prompt Injection Success Rate (%): Measure of vulnerability across models; lower is better.
- Time to Detect and Contain Attack (seconds): Average latency between prompt injection and response containment.
- Compliance and Data Leakage Incidents: Number of policy or data breaches detected during testing (target: zero).
- Model Integrity Score: Composite benchmark of safety, ethical compliance, and resilience across vendors.
- Time to Validate Vendor (days): Duration from test setup to validated evidence pack for procurement and governance teams
Validate AI Safety Before Deployment
Use NayaOne’s sandbox to test and benchmark AI models against jailbreak attacks, prompt injections, and data leakage risks – gaining hard evidence of resilience and compliance before production.
