Over the past decade working with security leaders and SOC teams across industries, I’ve seen the same pattern repeat itself across organizations of every size: security teams may have more visibility than ever before, yet analysts are still overwhelmed trying to determine which alerts actually matter.

Modern IT environments generate enormous volumes of telemetry across cloud platforms, SaaS applications, endpoints, networks, and identity systems. Each platform produces valuable signals, but the combined volume can overwhelm L1 SOC analysts who must decide which alerts require investigation.

This challenge is something we recently discussed with Blaine Brennecke, Director of Security Operations at Bottomline, during a customer conversation about .

“Security teams today are flooded with alerts,” Brennecke explained. “The challenge isn’t collecting more security data. It’s being able to analyze that data quickly enough to identify what actually matters.”

Bottomline’s experience reflects a broader shift happening across the industry. As their security team modernized its SOC environment, they partnered with WEI and AI-driven security automation provider Simbian to rethink how alerts are investigated, triaged, and prioritized.

Their journey highlights a reality many security leaders are now confronting: modernizing the SOC requires more than deploying new tools.

How the SOC Became a “Rube Goldberg Machine”

When I first began working closely with SOC teams and CISOs, most SecOps environments were relatively simple. Teams monitored a handful of core systems using a SIEM, endpoint protection tools, and basic network monitoring. But as today’s CISO’s know, average enterprise environments are much more intricate.

Organizations now operate across hybrid infrastructures that include , remote endpoints, SaaS applications, distributed workloads, and identity-driven access systems. Each environment generates its own telemetry, and analysts must correlate signals across all of them during an investigation.

Over time, the way many SOCs have evolved reminds me of a Rube Goldberg Machine, pictured below. New tools are deployed to solve legitimate visibility gaps, but each platform introduces its own alerts, dashboards, and investigation workflows. The system is an overly complex solution to a relatively straightforward problem. It is over designed and difficult to maintain… but make it less effective.��

Some tools integrate with each other. Some share data with the SIEM. But more often than not, the real integration layer ends up being the SOC analyst sitting in front of the screen.

SOC analysts frequently move between multiple systems just to gather enough context to determine whether activity represents a real threat. Investigations that should take minutes can take far longer when signals must be correlated manually across platforms.

The Operational Reality Inside Today’s SOC

During a recent , Senior Director of Security Operations at Bottomline, we discussed challenges that nearly every SOC leader we work with across the market recognizes.

Brennecke’s experience reflects a broader reality across the industry. SOC teams now have unprecedented visibility into their environments. But visibility alone doesn’t solve the operational challenge of detecting and responding to threats quickly enough.

Security analysts must still investigate alerts, correlate signals across tools, and determine whether suspicious activity represents a real attack.

At the same time, security leaders are being asked to improve detection and response capabilities while managing constrained budgets and limited staffing. As Brennecke put it, “A lot of organizations are in the same bucket today. Do more, do it faster, and do it with less.”

To address these challenges, Bottomline began evaluating ways to modernize its investigation workflows. That included exploring new approaches to automation and AI-driven alert analysis.

Working with WEI and Simbian, Bottomline introduced new investigation workflows that help analysts start their work with significantly more context around each alert.

Instead of manually stitching together data from multiple systems, analysts can begin investigations with a clearer picture of what’s happening across the environment.

The Challenges Driving SOC Modernization

Organizations attempting to modernize their SOCs typically encounter several common challenges.

Alert Fatigue: Security analysts may receive thousands of alerts each day from multiple detection tools. Without effective prioritization, distinguishing meaningful threats from routine activity becomes extremely difficult.

Tool Fragmentation: Security technologies deployed across network, endpoint, cloud, and identity environments often operate independently. Each platform produces its own alerts and dashboards, forcing analysts to gather context from multiple sources during an investigation.

Security Data Volume: This is growing as organizations expand their digital infrastructure. Traditional SIEM architectures can struggle to scale efficiently as log volumes increase.

Staffing Constraints: Experienced SOC analysts remain in high demand, and many organizations struggle to recruit and retain the talent needed to manage increasingly complex environments.

These operational pressures are forcing security leaders to rethink how their SOCs are designed and operated.

Why Technology Alone Doesn’t Solve the Problem

SIEM platforms, extended detection and response technologies, and emerging AI-driven investigation tools are helping SOC teams analyze large volumes of telemetry more efficiently. Technologies like Simbian’s AI-driven SOC automation platform can ingest alerts from existing security tools and perform automated investigation and triage steps that traditionally required significant analyst time.

When deployed effectively, these platforms reduce the number of alerts that require manual analysis while helping analysts focus on higher-priority threats.

But deploying new technology without rethinking workflows rarely delivers the results organizations expect.

Analysts still spend significant time investigating alerts manually because the surrounding processes and architecture haven’t evolved alongside the tools. That’s why successful SOC modernization efforts focus not just on technology, but also on architecture, operations, and engineering discipline.

Moving Security “Left of Bang”

WEI’s approach to SOC modernization focuses on helping organizations move their security posture Left of Bang. The concept refers to identifying and disrupting threats earlier in the attack lifecycle so security teams can prevent incidents before they cause operational damage.

Achieving this shift requires a combination of architecture design, technology integration, and operational optimization.

Our cybersecurity experts work closely with organizations to design architectures that unify telemetry across network, endpoint, identity, and cloud environments. This allows SOC teams to investigate threats with greater context and reduces unnecessary signals across multiple platforms.

We also focus heavily on how technologies integrate with one another. Security tools deliver the most value when analysts can move seamlessly between systems during investigations rather than manually stitching together context.

Operational workflows are another critical component. Automation and AI can dramatically reduce repetitive investigation tasks, allowing analysts to focus on deeper threat analysis rather than spending hours triaging alerts.

Through WEI’s demo and integration labs, organizations can also test new security architectures before deployment. This validation process helps reduce implementation risk and ensures that new technologies deliver measurable improvements to SOC operations.

Building the Modern SOC

As organizations like have discovered, SOC modernization is no longer optional. Attack surfaces continue to expand, and the amount of security data generated by modern infrastructure continues to grow. Security teams must adopt new approaches to detection and response if they want to keep pace with evolving threats.

must process large volumes of security data, prioritize high-risk threats, automate investigation workflows, and detect suspicious activity earlier in the attack lifecycle.

For many organizations, this shift is already underway.

“You’re no longer starting from square one,” Brennecke explained. “You’re starting 80 percent of the way down the triage pipeline.”

That change fundamentally alters how SOC analysts spend their time. Instead of sorting through large volumes of alerts, analysts can focus on deeper investigation and response activities.

Achieving this kind of transformation requires integrated architecture, operational alignment, and experienced engineering guidance. Organizations that take this approach are finding they can improve threat detection while reducing the operational burden placed on their SOC teams.

See How Bottomline Technologies Modernized Its SOC

Organizations evaluating SOC modernization initiatives often benefit from seeing how other security teams have approached similar challenges.

In our recent discussion with Bottomline Technologies, we explored how their security team partnered with WEI and Simbian to improve SOC visibility, reduce alert fatigue, and accelerate threat investigations across their environment.

Watch the full conversation to learn how Bottomline redesigned its SOC workflows and how new investigation models are helping analysts begin investigations nearly 80 percent of the way through the triage process.

Next Steps: Led by WEI’s cybersecurity experts and partnering with industry leaders, our cybersecurity assessments provide the insights needed to strengthen your defenses and ensure compliance. Whether you need to identify vulnerabilities, test your incident response capabilities, or develop a long-term security strategy, our team is here to help.

Contact WEI’s cybersecurity experts today to learn more about our assessments and discover how we can support your security goals. In the meantime,  featuring WEI cybersecurity assessments.

The post Lessons from Bottomline’s AI-Driven Security Operations appeared first on IT Solutions Provider - IT Consulting - Technology Solutions.

Most security leaders rely on Mean Time to Respond or Resolve (MTTR) as their primary board metric because it is measurable and easy to track. However, if MTTR is your only benchmark, you are underreporting the true impact of AI-driven security operations.

Threat volumes are rising as adversaries leverage AI, budgets remain constrained, and of incoming alerts. As a result, MTTR often reflects performance against limited exposure rather than total enterprise risk. To properly understand how to measure SOC ROI, leaders must expand their view and adopt broader SOC KPIs that account for coverage, analyst impact, and measurable risk reduction. Modern SOC automation solutions are changing the economics of detection and response, and your metrics must evolve accordingly.

Here are five KPIs executive leaders should prioritize.

1. Alert Coverage Rate

In many enterprise SOCs, only about 30 percent of alerts receive meaningful investigation due to manual triage limits. Alert Coverage Rate measures the percentage of total alerts fully reviewed.

If your team examines only a fraction of alerts, MTTR applies only to that fraction. AI-driven SOC automation solutions can correlate and prioritize alerts across EDR, SIEM, cloud, and identity tools, enabling near-complete coverage without increasing headcount. When assessing how to measure SOC ROI, start by asking whether you are reviewing all relevant signals.

2. False Positive Reduction and Analyst Lift

Alert fatigue creates operational and business risk. When junior analysts handle high volumes of noise, important signals can be missed. False Positive Reduction measures how effectively automation suppresses non-actionable alerts. Analyst Lift measures the increase in higher-value investigative work your team performs once repetitive triage is automated.

These SOC KPIs connect automation directly to business outcomes: fewer missed threats, stronger productivity, and improved workforce retention. Instead of hiring more entry-level analysts to manage queues, organizations can focus on deeper investigative expertise.

3. Time to Contain

MTTR measures ticket closure; Time to Contain measures how quickly malicious activity is isolated or neutralized. As adversaries compress attack timelines, containment speed directly affects financial exposure and regulatory risk. If SOC automation solutions initiate containment during triage, the potential blast radius is reduced immediately. Among modern SOC KPIs, Time to Contain provides a clearer measure of operational resilience than MTTR alone because it reflects proactive defense.

4. Detection Quality and Severity Accuracy

Not all alerts represent equal business impact. AI-driven triage that incorporates business context improves prioritization. Detection Quality tracks the percentage of true positives correctly identified. Severity Accuracy measures whether incident priority aligns with actual enterprise risk. For leaders evaluating how to measure SOC ROI, these metrics demonstrate improved decision precision. High-risk threats are surfaced faster, and resources are directed where they matter most.

5. Cost Per Alert and Cost Per Incident

Security investments must be financially defensible. Cost Per Alert divides the total SOC expense by the alerts investigated. Cost Per Incident measures the total cost per confirmed incident. When AI increases coverage and reduces manual workload, cost per alert declines even as protection expands.

If your SOC automation solutions reduce cost per incident while improving containment and detection accuracy, you have a strong ROI narrative.

Why MTTR Alone Falls Short

MTTR remains useful, but it does not capture unreviewed alerts, false positive suppression, containment speed, detection accuracy, or cost normalization. Modern SOC KPIs must reflect how AI reshapes security operations. When AI becomes an active participant in triage rather than just another tool, the conversation shifts from ticket management to enterprise risk reduction.

Final Thoughts

To understand how to measure SOC ROI, look beyond MTTR. Prioritize alert coverage, analyst lift, time to contain, detection accuracy, and cost per incident. AI expands coverage, sharpens prioritization, and drives measurable outcomes. Ready to demonstrate stronger ROI? Contact WEI to start the conversation.

Next Steps: In this exclusive WEI Tech Talk, cybersecurity leaders from WEI, Bottomline, and Simbian discuss how AI is changing the future of security operations and what it means for organizations trying to modernize their SOC.

Watch the full discussion below to hear practical insights from security practitioners and technology leaders working at the forefront of modern SOC transformation.

The post How to Measure SOC ROI: The KPIs in Addition to MTTR appeared first on IT Solutions Provider - IT Consulting - Technology Solutions.

SOC Directors are under pressure to reduce MTTR, cut alert fatigue, and justify security spending. While GenAI promises to accelerate detection and triage, the truth is simple: AI is only as strong as the data feeding it. Without clean, structured, and governed telemetry streams, even the best AI models deliver inconsistent results. Meanwhile, SIEM costs continue to climb.��

That’s where modern log pipelines come in. By combining AI-ready data practices with telemetry routing and optimization, SOC teams can reduce noise, strengthen investigations, and finally get ahead of the alert backlog. Cribl’s data pipeline solutions play a key role in this approach. Not as another tool, but as an upstream force multiplier that makes every downstream security investment more effective. 

Why Does AI Struggle in the SOC? 

Every SOC leader feels the pressure of rising workloads: 

• SIEM ingestion costs continue to rise 

• Analysts lose time chasing low-value alerts 

• Telemetry volumes are growing faster than budgets 

• Tool sprawl complicates detection engineering and reporting 

GenAI tools can accelerate triage and investigations, but without clean and consistent log data, AI models generate low-value outcomes. That means: 

• Inconsistent alert summaries 

• Incorrect correlations 

• Poor prioritization 

• “Hallucinated” context details 

• False confidence in incomplete information 

The root cause isn’t AI, but rather the noisy data feeding it. 

The AI-Ready SOC Starts With Telemetry You Can Trust 

Before automating investigations or deploying GenAI copilots, SOC teams need reliable pipelines that standardize, filter, enrich, and route logs based on value and relevance. 

This solves several long-standing challenges: 

1. Reduces SIEM Licensing Waste 

Most enterprises ingest logs they never use for detection or compliance. 
Modern routing allows SOCs to: 

• Keep low-value logs in budget-friendly storage 

• Send only meaningful data to the SIEM/XDR 

• Preserve full-fidelity raw logs for deep investigations 

2. Improves Detection Fidelity and Cuts False Positives 

According to the Ponemon Institute, analysts waste nearly 30% of their time responding to alerts that lack relevance or actionable context.  
 

With clean, normalized telemetry, AI-driven detectors and correlation engines can: 

• Identify patterns earlier 

• Reduce noise 

• Provide richer investigative context 

• Improve alert scoring 

How Cribl Stream and Cribl Edge Enable Data-Ready SOC Operations 

Cribl’s platform provides SOC teams with granular control over telemetry. This is a capability SIEMs and XDR platforms were never designed to deliver. 

Key strengths include: 

• Filtering noise before logs reach the SIEM 

• Routing data to multiple destinations based on cost, visibility, and policy 

• Normalization for clean, AI-ready datasets 

• Compression and enrichment for long-term data governance 

• Support for hybrid, multi-cloud, and distributed environments 

Cribl becomes the control plane for security data and the foundation for an AI-enhanced SOC.��

AI + Cribl: Better Together, Not Redundant 

Once telemetry is clean and optimized, AI tools finally reach their potential. SOC Directors gain measurable operational benefits: 

1. Faster Investigations: AI-driven enrichment + structured data = clear, actionable context in seconds. 

2. Smarter Alert Prioritization: Eliminating noise improves model accuracy and reduces false positives. 

3. Predictable SIEM Spending: Sending only valuable data to high-cost platforms keeps budgets aligned with business priorities. 

4. Better Audit-Ready Reporting: Consistent telemetry → reliable evidence → smoother compliance cycles. 

Data Modernization In Security 

SOC teams generate and store massive amounts of security data, but not all of it is useful and relevant. The challenge is determining what data to retain and how to store it cost-effectively. 

Rather than storing everything, AI in the SOC helps create smarter security logs by filtering out unnecessary data while preserving valuable insights. This data modernization has several benefits: 

• Better governance: AI categorizes data and retains only what’s relevant. 

• Efficient storage: AI-driven data summarization reduces log sizes without sacrificing critical information. 

• Improved query performance: Well-structured data enables faster searches and analysis. 

Organizations need reliable data processing solutions while maintaining compliance. Cribl supports this with tools like Cribl Stream and , which normalize and compress security logs before storage, reducing storage demands and helping maintain compliance.��

Optimizing Log Management For Efficiency 

As security data expands at an estimated 28% CAGR, organizations need to reevaluate their log management strategies. AI can play a key role in security operations by summarizing logs and reducing noise, making the vast amount of data more manageable. Smarter log management strategies include: 

• Log compression and truncation: AI reduces redundant data, lowering storage costs. 

• Dynamic retention policies: AI prioritizes storing logs that are critical for investigations while archiving less relevant data in cost-effective storage. 

• Automated data classification: AI categorizes logs based on security relevance, making retrieval easier. 

For example, AI can condense large volumes of NetFlow data from switches into a concise summary of key network activity. Cribl offers tools to support these strategies, enabling organizations to refine their log management strategies. With tools that help route logs intelligently and store high-volume logs in cost-effective locations, SOC teams can avoid overwhelming their SIEM and analytics systems while maintaining access to meaningful security insights.��

Final Thoughts 

GenAI is reshaping security operations by automating threat detection, improving alert triage, and optimizing data management. AI-driven threat detection reduces alert fatigue, while smarter security logs help SOC teams focus on valuable insights. As enterprises face growing cyber threats, integrating AI into security operations is now a practical requirement to address sophisticated attacks and data challenges. 

WEI’s team of cybersecurity experts helps organizations implement AI-driven SOC modernization strategies. From smarter log management to AI-powered automation, we guide enterprises in optimizing security workflows. If you’re looking to integrate AI-driven solutions in your SOC, reach out to WEI today and take the first step toward a more efficient security operation.��

Next Steps: Led by WEI’s cybersecurity experts and partnering with industry leaders, our cybersecurity assessments provide the insights needed to strengthen your defenses and ensure compliance. Whether you need to identify vulnerabilities, test your incident response capabilities, or develop a long-term security strategy, our team is here to help.

Contact WEI’s cybersecurity experts today to learn more about our assessments and discover how we can support your security goals. In the meantime,  featuring WEI cybersecurity assessments.

The post The Hidden Barrier to AI in the SOC: Unstructured, High-Cost Security Data  appeared first on IT Solutions Provider - IT Consulting - Technology Solutions.