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Effective FinOps: Moving from Recommendations to Risks

Eric Ethridge
DoiT

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action?

This shift isn't just about semantics; it's about survival. Unchecked cloud costs aren't minor annoyances; they're financial liabilities that can shatter a company's foundation. Startups can incinerate their monthly runway in a single day. For mid-market firms, unanticipated or overlooked cloud expenses can obliterate profitability in an instant. This financial bleed doesn't just erode profit; it undermines operational performance and even security.

FinOps teams must treat these financial risks with the same gravity as security threats. Security doesn't offer vague suggestions; it pinpoints specific risks, assigns severity levels and tracks vital metrics like mean time to resolution. By mirroring this approach, FinOps can evolve from passive optimization to a proactive financial defense system that tackles risks head-on. This transforms cloud cost management from an "if time allows" chore into the non-negotiable, timely imperative it must be.

Risks and Responsibilities

Changing the conversation from soft "recommendations" to hard "risks" ignites urgency and breeds accountability. Suddenly, teams aren't just trying to save a buck; they're active guardians of financial stability. Engineers attack cost issues with the same tenacity they apply to critical bugs, and finance teams are right there, collaborating every step of the way.

What do executives get?

Real outcomes measured by clear metrics, not just vague "what-ifs."

FinOps teams must adopt this aggressive mindset and equip themselves with the right tech. A solution's purpose isn't to churn out endless suggestions. It's to sharply identify cost risks, make them actionable and then track how quickly and effectively they're neutralized. This isn't about mere budget tweaking; it's about forging a culture where innovation flourishes alongside strong financial controls.

FinOps: A Force Multiplier for Security

This re-envisioned FinOps isn't just about money; it's a significant boost for security:

  • Shrinking the Attack Surface: FinOps excels at spotting overprovisioned or idle cloud resources. This isn't just cost savings; it's also a direct way to reduce potential targets for attackers and close security holes.
  • Hardwiring Accountability: The core of FinOps is accountability, a principle critical for security. When resources have clear owners, enforcing security policies becomes straightforward. Data breaches can be traced back to unauthorized usage, and better-managed environments drastically cut down on misconfigurations that create vulnerabilities.
  • Building a Security-First Culture: When cost accountability is deeply ingrained, employees at all levels are far more likely to stick to security best practices and proven configurations, knowing they're directly responsible.

Today's tools are powerful allies. The right cost and security monitoring platforms allow for side-by-side evaluation of critical data. Resource tags can simultaneously track both financial and security risks. Budget alerts can even flag potential security breaches by detecting unusual cost spikes caused by malicious activity. And automation can seamlessly integrate FinOps, consistently applying governance across financial and security landscapes.

A Better Picture

The current FinOps approach is falling short for far too many organizations. The deluge of "recommendations" isn't delivering. A radical reframing is desperately needed, one that starts by calling cost issues exactly what they are: actual risks, demanding swift urgency and clear accountability.

True success won't be found in the sheer number of suggestions, but in the critical issues unearthed and definitively resolved — and the speed and thoroughness with which that resolution occurs. This transformative approach doesn't just fix financial leaks; it shatters siloed decision-making, forcing crucial conversations where cost and security are always considered hand-in-hand, painting a far more complete and actionable picture.

Eric Ethridge is Senior Technical Account Manager at DoiT

Hot Topics

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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 gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Effective FinOps: Moving from Recommendations to Risks

Eric Ethridge
DoiT

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action?

This shift isn't just about semantics; it's about survival. Unchecked cloud costs aren't minor annoyances; they're financial liabilities that can shatter a company's foundation. Startups can incinerate their monthly runway in a single day. For mid-market firms, unanticipated or overlooked cloud expenses can obliterate profitability in an instant. This financial bleed doesn't just erode profit; it undermines operational performance and even security.

FinOps teams must treat these financial risks with the same gravity as security threats. Security doesn't offer vague suggestions; it pinpoints specific risks, assigns severity levels and tracks vital metrics like mean time to resolution. By mirroring this approach, FinOps can evolve from passive optimization to a proactive financial defense system that tackles risks head-on. This transforms cloud cost management from an "if time allows" chore into the non-negotiable, timely imperative it must be.

Risks and Responsibilities

Changing the conversation from soft "recommendations" to hard "risks" ignites urgency and breeds accountability. Suddenly, teams aren't just trying to save a buck; they're active guardians of financial stability. Engineers attack cost issues with the same tenacity they apply to critical bugs, and finance teams are right there, collaborating every step of the way.

What do executives get?

Real outcomes measured by clear metrics, not just vague "what-ifs."

FinOps teams must adopt this aggressive mindset and equip themselves with the right tech. A solution's purpose isn't to churn out endless suggestions. It's to sharply identify cost risks, make them actionable and then track how quickly and effectively they're neutralized. This isn't about mere budget tweaking; it's about forging a culture where innovation flourishes alongside strong financial controls.

FinOps: A Force Multiplier for Security

This re-envisioned FinOps isn't just about money; it's a significant boost for security:

  • Shrinking the Attack Surface: FinOps excels at spotting overprovisioned or idle cloud resources. This isn't just cost savings; it's also a direct way to reduce potential targets for attackers and close security holes.
  • Hardwiring Accountability: The core of FinOps is accountability, a principle critical for security. When resources have clear owners, enforcing security policies becomes straightforward. Data breaches can be traced back to unauthorized usage, and better-managed environments drastically cut down on misconfigurations that create vulnerabilities.
  • Building a Security-First Culture: When cost accountability is deeply ingrained, employees at all levels are far more likely to stick to security best practices and proven configurations, knowing they're directly responsible.

Today's tools are powerful allies. The right cost and security monitoring platforms allow for side-by-side evaluation of critical data. Resource tags can simultaneously track both financial and security risks. Budget alerts can even flag potential security breaches by detecting unusual cost spikes caused by malicious activity. And automation can seamlessly integrate FinOps, consistently applying governance across financial and security landscapes.

A Better Picture

The current FinOps approach is falling short for far too many organizations. The deluge of "recommendations" isn't delivering. A radical reframing is desperately needed, one that starts by calling cost issues exactly what they are: actual risks, demanding swift urgency and clear accountability.

True success won't be found in the sheer number of suggestions, but in the critical issues unearthed and definitively resolved — and the speed and thoroughness with which that resolution occurs. This transformative approach doesn't just fix financial leaks; it shatters siloed decision-making, forcing crucial conversations where cost and security are always considered hand-in-hand, painting a far more complete and actionable picture.

Eric Ethridge is Senior Technical Account Manager at DoiT

Hot Topics

The Latest

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 gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...