From Blocker to Enabler: How a Fintech Company Secured AI at Business Speed with Cato AI Security

Baltimore Aircoil Replaces MPLS with Cato, Improving Voice Quality, Enabling Video Conferencing, and Increasing Agility Summary: A financial technology company adopted AI early as a productivity and innovation accelerator. Cato AI Security helped the company enable AI usage at business speed while maintaining real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Key results Security posture maintained: The company maintained security scores above 99% while expanding AI usage. AI adoption enabled: Employees continued using AI tools without broad blocking. Visibility improved: Security gained insight into prompts, usage patterns, and AI interactions. Operational efficiency improved: The team avoided additional manual monitoring and security headcount. Governance expanded: Controls extended beyond employee-facing tools to internal bots and product AI features. Approvals accelerated: Improved visibility helped security evaluate and approve new AI applications faster. Challenge The company needed to enable rapid AI adoption without introducing data leakage, compliance, governance, or operational risk. AI was too valuable to block, but employee use of tools such as ChatGPT and Gemini created new exposure points. Traditional controls, including DLP, did not provide the prompt-level and behavior-level visibility required to understand what employees were asking AI tools or how those tools were being used. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or AI workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The company needed real-time, in-line controls that protected users without slowing innovation. Why Cato Cato AI Security was selected because it provided purpose-built AI security at the point of interaction. The solution sits between users and AI tools to monitor prompts, responses, behavior patterns, and data exposure risk in real time. This allowed the company to enable AI with guardrails instead of relying on broad blocking or manual review. Purpose-built AI security: Designed for prompt-level visibility and AI-specific risk, not retrofitted SaaS controls. In-line protection: Applies oversight and policy controls while employees use AI tools. Scalable governance: Extends controls across users, internal bots, and in-product AI features. Operational efficiency: Reduces the need for manual SOC/SIEM review and incremental security headcount. Continuous adaptation: Supports ongoing tuning as AI tools, LLMs, MCPs, and agentic workflows evolve. Impact Cato AI Security helped the company shift AI security from a blocker to an enabler. The company could move fast with AI while maintaining the visibility, controls, and confidence needed to protect the business. The security team gained the ability to observe, react, tune policies, and demonstrate to leadership and the company that controls were in place. Enabled secure AI adoption without disrupting business velocity. Improved board and company confidence through better visibility and auditability. Allowed the security team to invest in technology and controls rather than additional manual headcount. Established a foundation for securing future agentic AI and MCP-driven workflows.

CIAL Dun & Bradstreet Improves Networking and Security in Latin American with Cato Summary: A financial technology company adopted AI early as a productivity and innovation accelerator. Cato AI Security helped the company enable AI usage at business speed while maintaining real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Key results Security posture maintained: The company maintained security scores above 99% while expanding AI usage. AI adoption enabled: Employees continued using AI tools without broad blocking. Visibility improved: Security gained insight into prompts, usage patterns, and AI interactions. Operational efficiency improved: The team avoided additional manual monitoring and security headcount. Governance expanded: Controls extended beyond employee-facing tools to internal bots and product AI features. Approvals accelerated: Improved visibility helped security evaluate and approve new AI applications faster. Challenge The company needed to enable rapid AI adoption without introducing data leakage, compliance, governance, or operational risk. AI was too valuable to block, but employee use of tools such as ChatGPT and Gemini created new exposure points. Traditional controls, including DLP, did not provide the prompt-level and behavior-level visibility required to understand what employees were asking AI tools or how those tools were being used. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or AI workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The company needed real-time, in-line controls that protected users without slowing innovation. Why Cato Cato AI Security was selected because it provided purpose-built AI security at the point of interaction. The solution sits between users and AI tools to monitor prompts, responses, behavior patterns, and data exposure risk in real time. This allowed the company to enable AI with guardrails instead of relying on broad blocking or manual review. Purpose-built AI security: Designed for prompt-level visibility and AI-specific risk, not retrofitted SaaS controls. In-line protection: Applies oversight and policy controls while employees use AI tools. Scalable governance: Extends controls across users, internal bots, and in-product AI features. Operational efficiency: Reduces the need for manual SOC/SIEM review and incremental security headcount. Continuous adaptation: Supports ongoing tuning as AI tools, LLMs, MCPs, and agentic workflows evolve. Impact Cato AI Security helped the company shift AI security from a blocker to an enabler. The company could move fast with AI while maintaining the visibility, controls, and confidence needed to protect the business. The security team gained the ability to observe, react, tune policies, and demonstrate to leadership and the company that controls were in place. Enabled secure AI adoption without disrupting business velocity. Improved board and company confidence through better visibility and auditability. Allowed the security team to invest in technology and controls rather than additional manual headcount. Established a foundation for securing future agentic AI and MCP-driven workflows.

Diamond Braces Uses Cato to Boost WAN Security, Performance, and Reliability Summary: A financial technology company adopted AI early as a productivity and innovation accelerator. Cato AI Security helped the company enable AI usage at business speed while maintaining real-time visibility, governance, sensitive-data protection, and auditability across employee AI tools, internal bots, and in-product AI features. Key results Security posture maintained: The company maintained security scores above 99% while expanding AI usage. AI adoption enabled: Employees continued using AI tools without broad blocking. Visibility improved: Security gained insight into prompts, usage patterns, and AI interactions. Operational efficiency improved: The team avoided additional manual monitoring and security headcount. Governance expanded: Controls extended beyond employee-facing tools to internal bots and product AI features. Approvals accelerated: Improved visibility helped security evaluate and approve new AI applications faster. Challenge The company needed to enable rapid AI adoption without introducing data leakage, compliance, governance, or operational risk. AI was too valuable to block, but employee use of tools such as ChatGPT and Gemini created new exposure points. Traditional controls, including DLP, did not provide the prompt-level and behavior-level visibility required to understand what employees were asking AI tools or how those tools were being used. AI adoption was business-critical and could not be broadly restricted. Employees could unintentionally expose sensitive data through prompts or AI workflows. Existing tools lacked visibility into AI conversations, responses, and behavior patterns. The company needed real-time, in-line controls that protected users without slowing innovation. Why Cato Cato AI Security was selected because it provided purpose-built AI security at the point of interaction. The solution sits between users and AI tools to monitor prompts, responses, behavior patterns, and data exposure risk in real time. This allowed the company to enable AI with guardrails instead of relying on broad blocking or manual review. Purpose-built AI security: Designed for prompt-level visibility and AI-specific risk, not retrofitted SaaS controls. In-line protection: Applies oversight and policy controls while employees use AI tools. Scalable governance: Extends controls across users, internal bots, and in-product AI features. Operational efficiency: Reduces the need for manual SOC/SIEM review and incremental security headcount. Continuous adaptation: Supports ongoing tuning as AI tools, LLMs, MCPs, and agentic workflows evolve. Impact Cato AI Security helped the company shift AI security from a blocker to an enabler. The company could move fast with AI while maintaining the visibility, controls, and confidence needed to protect the business. The security team gained the ability to observe, react, tune policies, and demonstrate to leadership and the company that controls were in place. Enabled secure AI adoption without disrupting business velocity. Improved board and company confidence through better visibility and auditability. Allowed the security team to invest in technology and controls rather than additional manual headcount. Established a foundation for securing future agentic AI and MCP-driven workflows.