Cato AI Security Case Study

Baltimore Aircoil Replaces MPLS with Cato, Improving Voice Quality, Enabling Video Conferencing, and Increasing Agility Summary An anonymized fintech and cryptocurrency company adopted AI early to drive productivity and innovation. Deployed on AWS and powered by Amazon Bedrock, Cato AI Security enabled fast AI adoption while providing real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Customer snapshot The customer is a fintech company operating in a high-velocity, high-trust environment where innovation and security are equally critical. Executive sponsorship came from technology and security leadership, and the deployment covered employee AI tools, internal bots, and in-product AI features, including AWS-hosted AI workloads, Amazon Bedrock environments, AWS Prompts Storage integrations, and Amazon S3-based event or forensic storage. Key results Security posture maintained: Security scores stayed above 99%, reaching about 99.8%, even as AI use expanded. AI adoption enabled: Employees kept using AI tools without broad blocking. Visibility improved: Security teams gained insight into prompts, usage patterns, and AI interactions. Operational efficiency: The team avoided added manual monitoring and extra security headcount. Governance expanded: Controls extended to internal bots and product AI features. Approvals accelerated: Better visibility helped security approve new AI applications faster. Challenge The customer needed to enable rapid AI adoption without increasing data leakage, compliance, or operational risk. AI was too valuable to block, but tools such as ChatGPT and Gemini introduced new exposure points. Traditional controls like DLP lacked the prompt-level visibility needed to understand how employees were using AI. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The customer needed real-time controls that protected users without slowing innovation. Why Cato + AWS CatoAI runs on AWS, with all security processing, analytics, and data hosting within a single, enterprise-grade cloud environment. Amazon Bedrock powers real-time prompt analysis, sensitive data detection, and behavioral anomaly identification. Amazon S3 stores the forensic logs and audit trail regulators require. When customers build AI agents on Bedrock, CatoAI governs those environments agentlessly, with no additional software or configuration. At the enforcement layer, CatoAI sits inline between users and every AI tool, governing each prompt, response, and session in real time. Not retrospectively. Not by URL blocking. At the exact point AI risk occurs. Inline, prompt-level enforcement: CatoAI operates at the point of AI interaction; not retrospectively. Every prompt, response, and session is monitored and governed in real time, across all AI surfaces simultaneously. Real-time DLP: Sensitive data such as customer PII, financial data, proprietary code, and regulated information is detected and blocked before it leaves the organization through AI channels, with no manual review required. Agentic AI protection on AWS Bedrock: CatoAI integrates agentless into customer-deployed Bedrock environments, discovering, tracing, and governing AI agents the customer builds or runs on Bedrock with zero additional software or configuration overhead. Behavioral analytics and anomaly detection: NLP and security classification powered by Amazon Bedrock enables CatoAI to surface misuse patterns, shadow AI adoption, and policy violations before they become incidents, giving the security team proactive visibility rather than reactive investigation. Compliance-ready infrastructure: Deployed on AWS, all AI Security processing runs within a single, compliant cloud environment. Audit trails, forensic logs, and governance data are stored in Amazon S3 and accessible within the customer’s existing AWS environment — critical for regulatory oversight in a licensed cryptocurrency business. Continuous adaptation: As the AI landscape evolves; new LLMs, MCP servers, agentic frameworks, AI-native SaaS tools; CatoAI continuously updates detection models and policy controls. The customer’s security posture keeps pace with AI adoption without requiring ongoing manual reconfiguration. Impact Cato AI Security helped shift AI security from blocker to enabler. The customer gained the visibility, controls, and confidence to scale AI safely while demonstrating governance to leadership and customers. Enabled secure AI adoption without slowing the business. Improved board and customer confidence through visibility and auditability. Reduced reliance on manual security monitoring and added headcount. Created a foundation for securing future agentic AI workflows. Why AWS AWS provided the cloud foundation for deploying Cato AI Security at scale in a regulated fintech environment. AI processing, security analytics, and governance controls ran within a compliant cloud environment that simplified auditability and reduced infrastructure risk. Amazon Bedrock gave Cato AI Security access to leading foundation models for NLP and security classification, enabling real-time prompt analysis and behavioral anomaly detection. Cato AI Security also integrates agentlessly into customer-deployed Bedrock environments to apply inline governance and guardrails for AI agents. AWS Services Used AWS services used in the deployment included: Amazon Bedrock - Powered real-time prompt analysis, sensitive-data detection, and behavioral anomaly identification using foundation models for NLP and security classification. AWS Cloud Infrastructure - Hosted Cato AI Security processing, analytics, and governance controls in a resilient, scalable, and compliant cloud environment designed for regulated workloads. AWS Prompts Storage - Supported ingestion, storage, and analysis of prompt data from AWS-native environments to improve visibility, monitoring, and investigation. Amazon S3 - Supported event storage, export, and forensic log retention for security operations, audit support, and longer-term investigation needs. AWS deployment model - Kept AI security operations within a single auditable cloud environment, simplifying governance, data handling, and deployment consistency.

CIAL Dun & Bradstreet Improves Networking and Security in Latin American with Cato Summary An anonymized fintech and cryptocurrency company adopted AI early to drive productivity and innovation. Deployed on AWS and powered by Amazon Bedrock, Cato AI Security enabled fast AI adoption while providing real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Customer snapshot The customer is a fintech company operating in a high-velocity, high-trust environment where innovation and security are equally critical. Executive sponsorship came from technology and security leadership, and the deployment covered employee AI tools, internal bots, and in-product AI features, including AWS-hosted AI workloads, Amazon Bedrock environments, AWS Prompts Storage integrations, and Amazon S3-based event or forensic storage. Key results Security posture maintained: Security scores stayed above 99%, reaching about 99.8%, even as AI use expanded. AI adoption enabled: Employees kept using AI tools without broad blocking. Visibility improved: Security teams gained insight into prompts, usage patterns, and AI interactions. Operational efficiency: The team avoided added manual monitoring and extra security headcount. Governance expanded: Controls extended to internal bots and product AI features. Approvals accelerated: Better visibility helped security approve new AI applications faster. Challenge The customer needed to enable rapid AI adoption without increasing data leakage, compliance, or operational risk. AI was too valuable to block, but tools such as ChatGPT and Gemini introduced new exposure points. Traditional controls like DLP lacked the prompt-level visibility needed to understand how employees were using AI. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The customer needed real-time controls that protected users without slowing innovation. Why Cato + AWS CatoAI runs on AWS, with all security processing, analytics, and data hosting within a single, enterprise-grade cloud environment. Amazon Bedrock powers real-time prompt analysis, sensitive data detection, and behavioral anomaly identification. Amazon S3 stores the forensic logs and audit trail regulators require. When customers build AI agents on Bedrock, CatoAI governs those environments agentlessly, with no additional software or configuration. At the enforcement layer, CatoAI sits inline between users and every AI tool, governing each prompt, response, and session in real time. Not retrospectively. Not by URL blocking. At the exact point AI risk occurs. Inline, prompt-level enforcement: CatoAI operates at the point of AI interaction; not retrospectively. Every prompt, response, and session is monitored and governed in real time, across all AI surfaces simultaneously. Real-time DLP: Sensitive data such as customer PII, financial data, proprietary code, and regulated information is detected and blocked before it leaves the organization through AI channels, with no manual review required. Agentic AI protection on AWS Bedrock: CatoAI integrates agentless into customer-deployed Bedrock environments, discovering, tracing, and governing AI agents the customer builds or runs on Bedrock with zero additional software or configuration overhead. Behavioral analytics and anomaly detection: NLP and security classification powered by Amazon Bedrock enables CatoAI to surface misuse patterns, shadow AI adoption, and policy violations before they become incidents, giving the security team proactive visibility rather than reactive investigation. Compliance-ready infrastructure: Deployed on AWS, all AI Security processing runs within a single, compliant cloud environment. Audit trails, forensic logs, and governance data are stored in Amazon S3 and accessible within the customer’s existing AWS environment — critical for regulatory oversight in a licensed cryptocurrency business. Continuous adaptation: As the AI landscape evolves; new LLMs, MCP servers, agentic frameworks, AI-native SaaS tools; CatoAI continuously updates detection models and policy controls. The customer’s security posture keeps pace with AI adoption without requiring ongoing manual reconfiguration. Impact Cato AI Security helped shift AI security from blocker to enabler. The customer gained the visibility, controls, and confidence to scale AI safely while demonstrating governance to leadership and customers. Enabled secure AI adoption without slowing the business. Improved board and customer confidence through visibility and auditability. Reduced reliance on manual security monitoring and added headcount. Created a foundation for securing future agentic AI workflows. Why AWS AWS provided the cloud foundation for deploying Cato AI Security at scale in a regulated fintech environment. AI processing, security analytics, and governance controls ran within a compliant cloud environment that simplified auditability and reduced infrastructure risk. Amazon Bedrock gave Cato AI Security access to leading foundation models for NLP and security classification, enabling real-time prompt analysis and behavioral anomaly detection. Cato AI Security also integrates agentlessly into customer-deployed Bedrock environments to apply inline governance and guardrails for AI agents. AWS Services Used AWS services used in the deployment included: Amazon Bedrock - Powered real-time prompt analysis, sensitive-data detection, and behavioral anomaly identification using foundation models for NLP and security classification. AWS Cloud Infrastructure - Hosted Cato AI Security processing, analytics, and governance controls in a resilient, scalable, and compliant cloud environment designed for regulated workloads. AWS Prompts Storage - Supported ingestion, storage, and analysis of prompt data from AWS-native environments to improve visibility, monitoring, and investigation. Amazon S3 - Supported event storage, export, and forensic log retention for security operations, audit support, and longer-term investigation needs. AWS deployment model - Kept AI security operations within a single auditable cloud environment, simplifying governance, data handling, and deployment consistency.

Diamond Braces Uses Cato to Boost WAN Security, Performance, and Reliability Summary An anonymized fintech and cryptocurrency company adopted AI early to drive productivity and innovation. Deployed on AWS and powered by Amazon Bedrock, Cato AI Security enabled fast AI adoption while providing real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Customer snapshot The customer is a fintech company operating in a high-velocity, high-trust environment where innovation and security are equally critical. Executive sponsorship came from technology and security leadership, and the deployment covered employee AI tools, internal bots, and in-product AI features, including AWS-hosted AI workloads, Amazon Bedrock environments, AWS Prompts Storage integrations, and Amazon S3-based event or forensic storage. Key results Security posture maintained: Security scores stayed above 99%, reaching about 99.8%, even as AI use expanded. AI adoption enabled: Employees kept using AI tools without broad blocking. Visibility improved: Security teams gained insight into prompts, usage patterns, and AI interactions. Operational efficiency: The team avoided added manual monitoring and extra security headcount. Governance expanded: Controls extended to internal bots and product AI features. Approvals accelerated: Better visibility helped security approve new AI applications faster. Challenge The customer needed to enable rapid AI adoption without increasing data leakage, compliance, or operational risk. AI was too valuable to block, but tools such as ChatGPT and Gemini introduced new exposure points. Traditional controls like DLP lacked the prompt-level visibility needed to understand how employees were using AI. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The customer needed real-time controls that protected users without slowing innovation. Why Cato + AWS CatoAI runs on AWS, with all security processing, analytics, and data hosting within a single, enterprise-grade cloud environment. Amazon Bedrock powers real-time prompt analysis, sensitive data detection, and behavioral anomaly identification. Amazon S3 stores the forensic logs and audit trail regulators require. When customers build AI agents on Bedrock, CatoAI governs those environments agentlessly, with no additional software or configuration. At the enforcement layer, CatoAI sits inline between users and every AI tool, governing each prompt, response, and session in real time. Not retrospectively. Not by URL blocking. At the exact point AI risk occurs. Inline, prompt-level enforcement: CatoAI operates at the point of AI interaction; not retrospectively. Every prompt, response, and session is monitored and governed in real time, across all AI surfaces simultaneously. Real-time DLP: Sensitive data such as customer PII, financial data, proprietary code, and regulated information is detected and blocked before it leaves the organization through AI channels, with no manual review required. Agentic AI protection on AWS Bedrock: CatoAI integrates agentless into customer-deployed Bedrock environments, discovering, tracing, and governing AI agents the customer builds or runs on Bedrock with zero additional software or configuration overhead. Behavioral analytics and anomaly detection: NLP and security classification powered by Amazon Bedrock enables CatoAI to surface misuse patterns, shadow AI adoption, and policy violations before they become incidents, giving the security team proactive visibility rather than reactive investigation. Compliance-ready infrastructure: Deployed on AWS, all AI Security processing runs within a single, compliant cloud environment. Audit trails, forensic logs, and governance data are stored in Amazon S3 and accessible within the customer’s existing AWS environment — critical for regulatory oversight in a licensed cryptocurrency business. Continuous adaptation: As the AI landscape evolves; new LLMs, MCP servers, agentic frameworks, AI-native SaaS tools; CatoAI continuously updates detection models and policy controls. The customer’s security posture keeps pace with AI adoption without requiring ongoing manual reconfiguration. Impact Cato AI Security helped shift AI security from blocker to enabler. The customer gained the visibility, controls, and confidence to scale AI safely while demonstrating governance to leadership and customers. Enabled secure AI adoption without slowing the business. Improved board and customer confidence through visibility and auditability. Reduced reliance on manual security monitoring and added headcount. Created a foundation for securing future agentic AI workflows. Why AWS AWS provided the cloud foundation for deploying Cato AI Security at scale in a regulated fintech environment. AI processing, security analytics, and governance controls ran within a compliant cloud environment that simplified auditability and reduced infrastructure risk. Amazon Bedrock gave Cato AI Security access to leading foundation models for NLP and security classification, enabling real-time prompt analysis and behavioral anomaly detection. Cato AI Security also integrates agentlessly into customer-deployed Bedrock environments to apply inline governance and guardrails for AI agents. AWS Services Used AWS services used in the deployment included: Amazon Bedrock - Powered real-time prompt analysis, sensitive-data detection, and behavioral anomaly identification using foundation models for NLP and security classification. AWS Cloud Infrastructure - Hosted Cato AI Security processing, analytics, and governance controls in a resilient, scalable, and compliant cloud environment designed for regulated workloads. AWS Prompts Storage - Supported ingestion, storage, and analysis of prompt data from AWS-native environments to improve visibility, monitoring, and investigation. Amazon S3 - Supported event storage, export, and forensic log retention for security operations, audit support, and longer-term investigation needs. AWS deployment model - Kept AI security operations within a single auditable cloud environment, simplifying governance, data handling, and deployment consistency.