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New Cloudbrink Innovations Combine High-Performance ZTNA with Safe AI to Protect the Hybrid Workforce

Safe AI features unify policy and visibility capabilities for agentic AI, browser-based online AI services, and user-based access controls all on the same secure connectivity platform

Cloudbrink announced expanded security and performance benefits for AI agents and online AI services. 

The new AI capabilities are available on the same platform as Cloudbrink’s award-winning secure connectivity, allowing companies to secure users, apps, and AI in a more unified way.

According to a McKinsey report, 88 percent of enterprises globally are using AI for at least one business function. Along with this rapid AI adoption rate come the cybersecurity risks associated with AI, compounded by a diverse set of AI platforms and protocols and non-standardization. Not only do enterprises need to secure new vulnerabilities brought on by AI, they are fighting cybercriminals who have the power of AI as well.

“AI is complicating the threat map. Enterprises are using AI in multiple ways across disjointed paths and every path needs to be secured,” said Prakash Mana, CEO of Cloudbrink. “This becomes even more complex for companies with hybrid workforces. Last year was the year that companies dabbled in AI. In 2026 more enterprises are using AI for serious business and that requires security, scalability, and speed. That’s what Cloudbrink is providing for AI in the enterprise.”

New Cloudbrink AI Innovations

The Cloudbrink AI platform now allows users to secure AI in various forms. Developers are creating AI agents, browser-based online AI services, AI plugins, or their own AI models and LLMs for custom AI agents that are running locally. Each of these forms can result in sensitive data being sent and received via different network paths or different applications or services, creating a challenge for enterprises to secure users and sensitive data.

Cloudbrink’s new innovations include:

  • Safe AI BrinkAgent: The BrinkAgent component of Cloudbrink is equipped with AI intelligence that goes beyond responding to some network events. The BrinkAgent can recognize and understand various traffic from AI Agents and browser-based online AI services, and identify cases of sensitive data being leaked in either ways. The BrinkAgent takes actions in alignment with security policies defined according to the organization’s data protection and compliance requirements.
  • Built-in AI Agents/Services Definitions: Cloudbrink has created a built-in definitions database that can recognize a wide variety of AI Agent or online AI service protocols and platforms. With the definitions database enterprises can secure nearly every AI Agents/Services. Definitions are constantly updated to include new AI Agent types or protocols.
  • Custom AI Agent Definitions: For internally-developed AI Agents or custom, industry-specific solutions Cloudbrink allows customers to add custom AI Agents to the definitions database.
  • Unified Policy and Visibility: Customers can secure their hybrid workforce and secure AI Agents and online AI services all on the same management console, with visibility into everything related to users, apps, AI agents and services, as well as traffic details.

The new safe AI innovations build on Cloudbrink’s AI platform, which has gained traction with customers since its introduction last year. Customers are already experiencing the benefits of Cloudbrink’s AI innovations.

“The biggest barrier to enterprise adoption of agentic AI isn’t ROI - it’s trust,” said Siva Moduga, Co-Founder & CEO of Supervity AI. “Enterprises want AI systems that can autonomously execute critical operations, but they cannot compromise on security, data sovereignty, or performance. Supervity is building self-driving enterprise operations powered by AI Employees, and that requires a secure-by-design infrastructure foundation. Cloudbrink enables us to encrypt and isolate AI traffic end-to-end, protect access to private enterprise systems, and do so without introducing latency or operational friction.”

The updated Cloudbrink AI platform will be available next month. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

New Cloudbrink Innovations Combine High-Performance ZTNA with Safe AI to Protect the Hybrid Workforce

Safe AI features unify policy and visibility capabilities for agentic AI, browser-based online AI services, and user-based access controls all on the same secure connectivity platform

Cloudbrink announced expanded security and performance benefits for AI agents and online AI services. 

The new AI capabilities are available on the same platform as Cloudbrink’s award-winning secure connectivity, allowing companies to secure users, apps, and AI in a more unified way.

According to a McKinsey report, 88 percent of enterprises globally are using AI for at least one business function. Along with this rapid AI adoption rate come the cybersecurity risks associated with AI, compounded by a diverse set of AI platforms and protocols and non-standardization. Not only do enterprises need to secure new vulnerabilities brought on by AI, they are fighting cybercriminals who have the power of AI as well.

“AI is complicating the threat map. Enterprises are using AI in multiple ways across disjointed paths and every path needs to be secured,” said Prakash Mana, CEO of Cloudbrink. “This becomes even more complex for companies with hybrid workforces. Last year was the year that companies dabbled in AI. In 2026 more enterprises are using AI for serious business and that requires security, scalability, and speed. That’s what Cloudbrink is providing for AI in the enterprise.”

New Cloudbrink AI Innovations

The Cloudbrink AI platform now allows users to secure AI in various forms. Developers are creating AI agents, browser-based online AI services, AI plugins, or their own AI models and LLMs for custom AI agents that are running locally. Each of these forms can result in sensitive data being sent and received via different network paths or different applications or services, creating a challenge for enterprises to secure users and sensitive data.

Cloudbrink’s new innovations include:

  • Safe AI BrinkAgent: The BrinkAgent component of Cloudbrink is equipped with AI intelligence that goes beyond responding to some network events. The BrinkAgent can recognize and understand various traffic from AI Agents and browser-based online AI services, and identify cases of sensitive data being leaked in either ways. The BrinkAgent takes actions in alignment with security policies defined according to the organization’s data protection and compliance requirements.
  • Built-in AI Agents/Services Definitions: Cloudbrink has created a built-in definitions database that can recognize a wide variety of AI Agent or online AI service protocols and platforms. With the definitions database enterprises can secure nearly every AI Agents/Services. Definitions are constantly updated to include new AI Agent types or protocols.
  • Custom AI Agent Definitions: For internally-developed AI Agents or custom, industry-specific solutions Cloudbrink allows customers to add custom AI Agents to the definitions database.
  • Unified Policy and Visibility: Customers can secure their hybrid workforce and secure AI Agents and online AI services all on the same management console, with visibility into everything related to users, apps, AI agents and services, as well as traffic details.

The new safe AI innovations build on Cloudbrink’s AI platform, which has gained traction with customers since its introduction last year. Customers are already experiencing the benefits of Cloudbrink’s AI innovations.

“The biggest barrier to enterprise adoption of agentic AI isn’t ROI - it’s trust,” said Siva Moduga, Co-Founder & CEO of Supervity AI. “Enterprises want AI systems that can autonomously execute critical operations, but they cannot compromise on security, data sovereignty, or performance. Supervity is building self-driving enterprise operations powered by AI Employees, and that requires a secure-by-design infrastructure foundation. Cloudbrink enables us to encrypt and isolate AI traffic end-to-end, protect access to private enterprise systems, and do so without introducing latency or operational friction.”

The updated Cloudbrink AI platform will be available next month. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.