Cato AI Security + AWS: Protecting Patient Data in the Age of GenAI

Baltimore Aircoil Replaces MPLS with Cato, Improving Voice Quality, Enabling Video Conferencing, and Increasing Agility Summary A digital healthcare provider needed to adopt GenAI without exposing protected health information (PHI) or disrupting clinical workflows. Cato AI Security, deployed on AWS and powered by Amazon Bedrock, delivered real-time PHI protection, shadow AI discovery, and healthcare-specific policy enforcement across every AI interaction. The result: governed AI adoption at clinical speed, with full HIPAA accountability and no compromise on care quality. Customer Snapshot An anonymized digital healthcare provider operating in a regulated environment needed to secure enterprise GenAI adoption while protecting protected health information (PHI) and maintaining HIPAA alignment. Primary risks included PHI exposure, shadow AI, clinical workflow misuse, and auditability gaps across a distributed clinical and operational workforce. The engagement is aligned to the AWS AI Security competency, covering visibility, governance, sensitive data protection, policy enforcement, and threat prevention. AWS services used include Amazon Bedrock, AWS Cloud Infrastructure, AWS Prompts Storage, and Amazon S3. Key results Discovered and remediated unapproved AI tools: websites, browser extensions, and IDE plugins. Blocked PHI and confidential records from reaching public GenAI models. Enforced healthcare-specific AI policies across clinical and operational workflows. Delivered reporting and analytics for AI usage, risk behavior, and policy violations. Security team repositioned as an AI enablement partner, not a blocker. Challenge Rapid GenAI adoption outpaced existing security controls. Employees could share PHI through AI prompts, adopt unsanctioned tools, or introduce AI into clinical workflows without oversight. Without real-time enforcement at the point of AI interaction, the organisation faced data privacy exposure, HIPAA risk, and patient safety implications. Customer Goals Enable responsible GenAI adoption without broad blocking or productivity loss. Protect PHI and confidential records from exposure to public or unmanaged AI tools. Detect and remediate shadow AI across websites, browser extensions, and developer tools. Enforce healthcare-specific policies for clinical and non-clinical AI workflows. Provide reporting, analytics, and audit evidence for compliance and incident response. Impact The customer moved from unmanaged AI risk to governed AI adoption. Compliance teams gained visibility, enforcement, and an audit trail that satisfies regulators. Clinicians and staff got safe GenAI access. Patient data stayed protected. Reduced operational burden: Automated PHI detection and policy enforcement cut manual SOC and compliance review effort. Lower risk exposure: Preventing PHI leakage and compliance violations avoids regulatory penalties and reputational damage. Faster time to value: Immediate visibility into AI usage and shadow AI risk from day one, with no lengthy implementation cycle. Productivity preserved: Responsible AI enablement replaced blanket restrictions, keeping clinicians and staff productive. Scalable governance: The framework extends to new AI tools, users, and workflows as the AI footprint grows. Why Cato + AWS Cato AI Security sits inline between users and AI tools, monitoring every prompt, response, and session in real time without agents or workflow friction. Deployed on AWS, PHI processing and governance controls run on HIPAA-eligible infrastructure. Amazon Bedrock powers semantic PHI detection and clinical content classification at a depth rule-based tools cannot match. AWS Prompts Storage feeds the Cato AI Security data pipeline for prompt-level visibility, and Amazon S3 delivers the audit trail compliance teams need for HIPAA accountability. PHI blocked at source. Semantic detection stops patient data reaching public AI models before exposure, not after. Shadow AI eliminated. Unsanctioned tools across websites, browser extensions, and IDEs discovered, inventoried, and remediated. Clinical guardrails enforced. Context-aware policies prevent inappropriate AI use in patient-care workflows, built for healthcare, not retrofitted. HIPAA audit trail on Amazon S3. AI interactions and policy violations logged for incident investigation and regulatory accountability. Agentless. Zero friction. No endpoint software, no new consoles. Deployed via proxy interception and native integrations. AWS Services Used Amazon Bedrock: NLP and security classification for semantic PHI detection, clinical content analysis, and behavioural anomaly identification; agentless integration into customer-deployed Bedrock environments. AWS Cloud Infrastructure: HIPAA-eligible cloud computing and data hosting for all Cato AI Security AI Security processing and governance. AWS Prompts Storage: Prompt data ingestion and governance across the customer’s AI environment. Amazon S3: Security event storage, forensic logs, and HIPAA-compliant audit trails. AWS Infrastructure (Deployed on AWS): Cato AI Security AI Security is Deployed on AWS, providing a compliant, high-availability cloud environment for PHI processing, policy enforcement, and audit logging.

CIAL Dun & Bradstreet Improves Networking and Security in Latin American with Cato Summary A digital healthcare provider needed to adopt GenAI without exposing protected health information (PHI) or disrupting clinical workflows. Cato AI Security, deployed on AWS and powered by Amazon Bedrock, delivered real-time PHI protection, shadow AI discovery, and healthcare-specific policy enforcement across every AI interaction. The result: governed AI adoption at clinical speed, with full HIPAA accountability and no compromise on care quality. Customer Snapshot An anonymized digital healthcare provider operating in a regulated environment needed to secure enterprise GenAI adoption while protecting protected health information (PHI) and maintaining HIPAA alignment. Primary risks included PHI exposure, shadow AI, clinical workflow misuse, and auditability gaps across a distributed clinical and operational workforce. The engagement is aligned to the AWS AI Security competency, covering visibility, governance, sensitive data protection, policy enforcement, and threat prevention. AWS services used include Amazon Bedrock, AWS Cloud Infrastructure, AWS Prompts Storage, and Amazon S3. Key results Discovered and remediated unapproved AI tools: websites, browser extensions, and IDE plugins. Blocked PHI and confidential records from reaching public GenAI models. Enforced healthcare-specific AI policies across clinical and operational workflows. Delivered reporting and analytics for AI usage, risk behavior, and policy violations. Security team repositioned as an AI enablement partner, not a blocker. Challenge Rapid GenAI adoption outpaced existing security controls. Employees could share PHI through AI prompts, adopt unsanctioned tools, or introduce AI into clinical workflows without oversight. Without real-time enforcement at the point of AI interaction, the organisation faced data privacy exposure, HIPAA risk, and patient safety implications. Customer Goals Enable responsible GenAI adoption without broad blocking or productivity loss. Protect PHI and confidential records from exposure to public or unmanaged AI tools. Detect and remediate shadow AI across websites, browser extensions, and developer tools. Enforce healthcare-specific policies for clinical and non-clinical AI workflows. Provide reporting, analytics, and audit evidence for compliance and incident response. Impact The customer moved from unmanaged AI risk to governed AI adoption. Compliance teams gained visibility, enforcement, and an audit trail that satisfies regulators. Clinicians and staff got safe GenAI access. Patient data stayed protected. Reduced operational burden: Automated PHI detection and policy enforcement cut manual SOC and compliance review effort. Lower risk exposure: Preventing PHI leakage and compliance violations avoids regulatory penalties and reputational damage. Faster time to value: Immediate visibility into AI usage and shadow AI risk from day one, with no lengthy implementation cycle. Productivity preserved: Responsible AI enablement replaced blanket restrictions, keeping clinicians and staff productive. Scalable governance: The framework extends to new AI tools, users, and workflows as the AI footprint grows. Why Cato + AWS Cato AI Security sits inline between users and AI tools, monitoring every prompt, response, and session in real time without agents or workflow friction. Deployed on AWS, PHI processing and governance controls run on HIPAA-eligible infrastructure. Amazon Bedrock powers semantic PHI detection and clinical content classification at a depth rule-based tools cannot match. AWS Prompts Storage feeds the Cato AI Security data pipeline for prompt-level visibility, and Amazon S3 delivers the audit trail compliance teams need for HIPAA accountability. PHI blocked at source. Semantic detection stops patient data reaching public AI models before exposure, not after. Shadow AI eliminated. Unsanctioned tools across websites, browser extensions, and IDEs discovered, inventoried, and remediated. Clinical guardrails enforced. Context-aware policies prevent inappropriate AI use in patient-care workflows, built for healthcare, not retrofitted. HIPAA audit trail on Amazon S3. AI interactions and policy violations logged for incident investigation and regulatory accountability. Agentless. Zero friction. No endpoint software, no new consoles. Deployed via proxy interception and native integrations. AWS Services Used Amazon Bedrock: NLP and security classification for semantic PHI detection, clinical content analysis, and behavioural anomaly identification; agentless integration into customer-deployed Bedrock environments. AWS Cloud Infrastructure: HIPAA-eligible cloud computing and data hosting for all Cato AI Security AI Security processing and governance. AWS Prompts Storage: Prompt data ingestion and governance across the customer’s AI environment. Amazon S3: Security event storage, forensic logs, and HIPAA-compliant audit trails. AWS Infrastructure (Deployed on AWS): Cato AI Security AI Security is Deployed on AWS, providing a compliant, high-availability cloud environment for PHI processing, policy enforcement, and audit logging.

Diamond Braces Uses Cato to Boost WAN Security, Performance, and Reliability Summary A digital healthcare provider needed to adopt GenAI without exposing protected health information (PHI) or disrupting clinical workflows. Cato AI Security, deployed on AWS and powered by Amazon Bedrock, delivered real-time PHI protection, shadow AI discovery, and healthcare-specific policy enforcement across every AI interaction. The result: governed AI adoption at clinical speed, with full HIPAA accountability and no compromise on care quality. Customer Snapshot An anonymized digital healthcare provider operating in a regulated environment needed to secure enterprise GenAI adoption while protecting protected health information (PHI) and maintaining HIPAA alignment. Primary risks included PHI exposure, shadow AI, clinical workflow misuse, and auditability gaps across a distributed clinical and operational workforce. The engagement is aligned to the AWS AI Security competency, covering visibility, governance, sensitive data protection, policy enforcement, and threat prevention. AWS services used include Amazon Bedrock, AWS Cloud Infrastructure, AWS Prompts Storage, and Amazon S3. Key results Discovered and remediated unapproved AI tools: websites, browser extensions, and IDE plugins. Blocked PHI and confidential records from reaching public GenAI models. Enforced healthcare-specific AI policies across clinical and operational workflows. Delivered reporting and analytics for AI usage, risk behavior, and policy violations. Security team repositioned as an AI enablement partner, not a blocker. Challenge Rapid GenAI adoption outpaced existing security controls. Employees could share PHI through AI prompts, adopt unsanctioned tools, or introduce AI into clinical workflows without oversight. Without real-time enforcement at the point of AI interaction, the organisation faced data privacy exposure, HIPAA risk, and patient safety implications. Customer Goals Enable responsible GenAI adoption without broad blocking or productivity loss. Protect PHI and confidential records from exposure to public or unmanaged AI tools. Detect and remediate shadow AI across websites, browser extensions, and developer tools. Enforce healthcare-specific policies for clinical and non-clinical AI workflows. Provide reporting, analytics, and audit evidence for compliance and incident response. Impact The customer moved from unmanaged AI risk to governed AI adoption. Compliance teams gained visibility, enforcement, and an audit trail that satisfies regulators. Clinicians and staff got safe GenAI access. Patient data stayed protected. Reduced operational burden: Automated PHI detection and policy enforcement cut manual SOC and compliance review effort. Lower risk exposure: Preventing PHI leakage and compliance violations avoids regulatory penalties and reputational damage. Faster time to value: Immediate visibility into AI usage and shadow AI risk from day one, with no lengthy implementation cycle. Productivity preserved: Responsible AI enablement replaced blanket restrictions, keeping clinicians and staff productive. Scalable governance: The framework extends to new AI tools, users, and workflows as the AI footprint grows. Why Cato + AWS Cato AI Security sits inline between users and AI tools, monitoring every prompt, response, and session in real time without agents or workflow friction. Deployed on AWS, PHI processing and governance controls run on HIPAA-eligible infrastructure. Amazon Bedrock powers semantic PHI detection and clinical content classification at a depth rule-based tools cannot match. AWS Prompts Storage feeds the Cato AI Security data pipeline for prompt-level visibility, and Amazon S3 delivers the audit trail compliance teams need for HIPAA accountability. PHI blocked at source. Semantic detection stops patient data reaching public AI models before exposure, not after. Shadow AI eliminated. Unsanctioned tools across websites, browser extensions, and IDEs discovered, inventoried, and remediated. Clinical guardrails enforced. Context-aware policies prevent inappropriate AI use in patient-care workflows, built for healthcare, not retrofitted. HIPAA audit trail on Amazon S3. AI interactions and policy violations logged for incident investigation and regulatory accountability. Agentless. Zero friction. No endpoint software, no new consoles. Deployed via proxy interception and native integrations. AWS Services Used Amazon Bedrock: NLP and security classification for semantic PHI detection, clinical content analysis, and behavioural anomaly identification; agentless integration into customer-deployed Bedrock environments. AWS Cloud Infrastructure: HIPAA-eligible cloud computing and data hosting for all Cato AI Security AI Security processing and governance. AWS Prompts Storage: Prompt data ingestion and governance across the customer’s AI environment. Amazon S3: Security event storage, forensic logs, and HIPAA-compliant audit trails. AWS Infrastructure (Deployed on AWS): Cato AI Security AI Security is Deployed on AWS, providing a compliant, high-availability cloud environment for PHI processing, policy enforcement, and audit logging.