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The PADS Framework for Compliance and Security - Part 2

Gabriel Lowy

Start with The PADS Framework for Compliance and Security - Part 1

Below we highlight new regulations in the global securities industry that underscore the risks companies face when they don’t have a good handle on user experience or application performance across the application delivery chain.

Capital Markets: Pressure to Avoid Market Disruption

Global securities markets have become increasingly reliant on technology and automated systems that operate at light speed. But in recent years, these systems have suffered both minor glitches and major outages. They have also been susceptible to cyberattacks, further underscoring their vulnerability.

To ensure the integrity and resilience of IT systems and reduce the severity and frequency of these disruptions, the Securities and Exchange Commission (SEC) adopted Regulation Systems Compliance and Integrity (Regulation SCI) in November 2015. The regulation applies to so-called SCI entities, including national securities exchanges, certain high-volume alternative trading systems, clearing agencies, plan processors and self-regulatory agencies such as the Financial Industry Regulatory Authority (FINRA) and the Municipal Securities Rulemaking Board (MSRB).

Covered entities must design, develop, test, maintain and monitor their operational systems according to Regulation SCI's standards and best practices. These policies apply to nine IT and security domains:


Source: SEC, Tech-Tonics Advisors

Regulation SCI requires new reporting and disclosure of disruptions, intrusions and other adverse events – with special emphasis on customer personal information. There are also new requirements to notify affected customers and plan participants if the events are "major" or involve "critical SCI systems."

Covered entities must perform ongoing audits and risk assessments. This includes evaluating IT governance services performed by specific entities. Material changes to any “SCI system” – whether existing or planned – must be reported on a quarterly basis. If any covered entity does not implement compliant controls or neglects to report failures to the SEC they could be subject to legal action.

Beyond testing requirements built into business continuity and disaster recovery standards, Regulation SCI also mandates industry-wide coordinated testing to ensure systems-wide functionality and safety. While testing has already begun, the industry has until November 2016 to get processes in place.

Market disruptions have resulted in extreme volatility, fractured investor confidence, catastrophic losses and unprecedented fines for compliance violations. Intelligence across the entire application delivery chain is essential for all covered entities to comply with Regulation SCI.

Conclusion

In the software-defined economy application performance and user experience are critical differentiators to drive business and risk management objectives. The risks of poor application performance and user experience include business interruption, eroding employee engagement and customer satisfaction, regulatory noncompliance and reputational damage.

The underbelly of modern distributed computing environments is growing regulatory oversight pertaining to systems efficacy and security. While regulations are nuanced to specific industries, the connectivity and interdependencies of systems are similar across all sectors. Regulators are increasingly focused on these relationships – and the underlying systems and applications – that comprise application delivery chains.

More companies are incorporating cloud, mobile and social into computing architectures, business plans and processes. With the growth of containers and microservices, coupled with the emerging Internet of Things (IoT), it is imperative for IT teams and senior management to embrace the strategic importance of user experience and application performance to achieve ROI and risk management objectives.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The PADS Framework for Compliance and Security - Part 2

Gabriel Lowy

Start with The PADS Framework for Compliance and Security - Part 1

Below we highlight new regulations in the global securities industry that underscore the risks companies face when they don’t have a good handle on user experience or application performance across the application delivery chain.

Capital Markets: Pressure to Avoid Market Disruption

Global securities markets have become increasingly reliant on technology and automated systems that operate at light speed. But in recent years, these systems have suffered both minor glitches and major outages. They have also been susceptible to cyberattacks, further underscoring their vulnerability.

To ensure the integrity and resilience of IT systems and reduce the severity and frequency of these disruptions, the Securities and Exchange Commission (SEC) adopted Regulation Systems Compliance and Integrity (Regulation SCI) in November 2015. The regulation applies to so-called SCI entities, including national securities exchanges, certain high-volume alternative trading systems, clearing agencies, plan processors and self-regulatory agencies such as the Financial Industry Regulatory Authority (FINRA) and the Municipal Securities Rulemaking Board (MSRB).

Covered entities must design, develop, test, maintain and monitor their operational systems according to Regulation SCI's standards and best practices. These policies apply to nine IT and security domains:


Source: SEC, Tech-Tonics Advisors

Regulation SCI requires new reporting and disclosure of disruptions, intrusions and other adverse events – with special emphasis on customer personal information. There are also new requirements to notify affected customers and plan participants if the events are "major" or involve "critical SCI systems."

Covered entities must perform ongoing audits and risk assessments. This includes evaluating IT governance services performed by specific entities. Material changes to any “SCI system” – whether existing or planned – must be reported on a quarterly basis. If any covered entity does not implement compliant controls or neglects to report failures to the SEC they could be subject to legal action.

Beyond testing requirements built into business continuity and disaster recovery standards, Regulation SCI also mandates industry-wide coordinated testing to ensure systems-wide functionality and safety. While testing has already begun, the industry has until November 2016 to get processes in place.

Market disruptions have resulted in extreme volatility, fractured investor confidence, catastrophic losses and unprecedented fines for compliance violations. Intelligence across the entire application delivery chain is essential for all covered entities to comply with Regulation SCI.

Conclusion

In the software-defined economy application performance and user experience are critical differentiators to drive business and risk management objectives. The risks of poor application performance and user experience include business interruption, eroding employee engagement and customer satisfaction, regulatory noncompliance and reputational damage.

The underbelly of modern distributed computing environments is growing regulatory oversight pertaining to systems efficacy and security. While regulations are nuanced to specific industries, the connectivity and interdependencies of systems are similar across all sectors. Regulators are increasingly focused on these relationships – and the underlying systems and applications – that comprise application delivery chains.

More companies are incorporating cloud, mobile and social into computing architectures, business plans and processes. With the growth of containers and microservices, coupled with the emerging Internet of Things (IoT), it is imperative for IT teams and senior management to embrace the strategic importance of user experience and application performance to achieve ROI and risk management objectives.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...