Why Security and Observability Must Work Together in 2026

Security and Observability Challenges

The problem is context.

Security teams may see suspicious behavior without understanding the application or infrastructure conditions surrounding it. IT operations teams may see a performance problem without recognizing that malicious activity contributed to it. Network, cloud and engineering teams may each hold a different piece of the same incident, but those pieces remain scattered across tools, dashboards, and workflows.

That fragmentation creates blind spots, slows investigation and increases operational burden at the exact moment organizations need to move faster. As environments become more distributed and threats become more sophisticated, security and observability can no longer operate as separate disciplines. They must work together to create a shared, end-to-end view of risk, performance, and business impact.

This is the foundation of digital resilience: the ability to understand what is happening, determine what matters, and respond before a technical issue or security event becomes a business disruption.

More Data Does Not Automatically Mean More Visibility

Hybrid environments have expanded the number and variety of systems that teams must protect and operate. Applications may span private data centers, public clouds, SaaS platforms, remote endpoints, APIs, and third-party services. At the same time, employees, customers, partners, and machine identities all interact with those environments in different ways.

Traditional monitoring often provides a view into individual components. A network tool can show latency. An application dashboard can report an error. A security platform can flag an unusual login. Each signal may be useful, but no single one explains the entire situation.

Observability helps teams move beyond isolated events by connecting logs, metrics, traces, and other telemetry across the environment. The goal is not simply to collect more information. It is to make that information usable; so, teams can understand relationships, identify root causes, and determine how technical conditions affect users and the business.

That distinction matters. A slowdown in an application could be a code defect, an infrastructure constraint, a network issue, or an early indication of malicious activity. An authentication spike could reflect expected demand, automated abuse, or compromised credentials. Without context, teams must manually compare systems and reconstruct timelines while an issue continues to unfold.

Splunk's State of Observability 2025 illustrates the scale of this challenge. Fifty-nine percent of respondents said they contend with too many disparate tools, while 52% reported a high volume of false-positive alerts. The same research found that stronger collaboration between observability and security teams is associated with better outcomes: 64% of respondents reported fewer application and infrastructure performance issues because of that collaboration.

The lesson is clear. Visibility is not achieved by adding another dashboard. It comes from connecting data across domains and giving teams the context to interpret it together.

Security Incidents Rarely Stay Inside Security

A cyberattack may begin with an identity, endpoint or application, but its impact can spread across the technology environment. Compromised credentials can be used to access cloud services. An attacker can move laterally through trusted systems. Ransomware can disrupt infrastructure and business operations. Abuse of an API can expose data while appearing similar to legitimate application traffic.

The threat landscape continues to evolve as attackers exploit trusted identities, widely used tools, cloud services, APIs and software dependencies. Many of today’s most disruptive attacks are designed to blend into legitimate activity and move quickly across environments, making them difficult to detect through isolated security signals alone.

These challenges are reflected in the threats highlighted in Splunk and C1's Top Cybersecurity Threats to Watch in 2026 brief:

• AI-powered social engineering that makes phishing, vishing and impersonation more convincing and scalable.

• Identity-first intrusions that use stolen tokens, MFA fatigue or overprivileged accounts to enter through the front door.

• Lateral movement across cloud and SaaS environments using legitimate credentials and APIs.

• Multi-extortion ransomware campaigns that combine encryption, data theft, harassment, and regulatory pressure.

• Software and supply chain attacks that turn trusted vendors, updates, and dependencies into attack paths.

• API-centric attacks that exploit weak authentication, excessive permissions and undocumented endpoints.

• Living-off-the-land techniques that abuse native administrative tools and blend into normal activity.

• Rapid exploitation of newly disclosed vulnerabilities, sometimes within hours of disclosure.

What connects these threats is their ability to hide within legitimate activity. They exploit trust, use existing tools, and cross boundaries between identities, applications, networks, and cloud platforms. A security alert alone may not provide enough evidence to distinguish a real attack from normal behavior. Teams need the surrounding operational context: what changed, which services were affected, how the user or workload typically behaves, and whether related anomalies appeared elsewhere.

That is where observability becomes a security advantage. Telemetry from applications and infrastructure can add context to an investigation. Security data can help an operations team recognize when a reliability problem may have a malicious cause. When teams share information and workflows, they can identify the full scope of an incident sooner and coordinate a more effective response.

Alert Volume Is an Operational Problem

Security and operations teams are often measured on speed: mean time to detect, mean time to investigate, mean time to respond, and mean time to restore service. Yet many analysts spend their time on work that does not improve any of those outcomes.

They maintain tools, switch between consoles, enrich alerts, collect files and URLs, compare timestamps, and manually assemble evidence. These steps are necessary when systems are disconnected, but they consume the time and attention that should be focused on judgment and response.

Splunk's State of Security 2025 found that 46% of respondents spend more time maintaining tools than defending their organizations. Fifty-nine percent said they have too many alerts, 55% deal with too many false positives and 57% lose valuable investigation time because of gaps in their data management strategies. The report also found that 78% consider their security tools dispersed and disconnected.

This is not simply an analyst productivity issue. It is an enterprise risk issue. When every alert demands manual work, high-priority activity can remain buried in noise. Investigations take longer. Response becomes inconsistent. Experienced analysts become overloaded, while less experienced team members may lack the context needed to confidently reach a decision.

Automation can reduce this burden when it is applied to the right work. Repetitive processes such as collecting technical evidence, detonating suspicious content, enriching indicators, correlating related events, and initiating response playbooks can often be automated or accelerated. That gives analysts more time to validate findings, assess business impact, and make decisions that require human expertise.

Splunk Attack Analyzer is one example. It automatically analyzes complex credential-phishing and malware threats, safely interacts with malicious content and shows analysts the technical steps of an attack. When paired with Splunk SOAR, automated analysis can feed response workflows without requiring analysts to manually perform every investigative task or build complex playbooks across multiple tools.

The goal is not to remove people from security operations. It is to remove unnecessary friction from their work. Effective automation combines machine speed with human judgment, producing faster and more consistent investigations while keeping analysts in control of consequential decisions.

Why Security and Observability Belong in the Same Operating Model

Bringing security and observability together does not mean merging every team or replacing every tool. It means designing shared data, context and workflows around the outcomes both functions support.

Those shared outcomes include:

• Faster detection. Broader telemetry makes it easier to recognize patterns that would be difficult to see within one domain.

• More efficient investigation. Correlated operational and security context reduces the time spent gathering evidence and determining scope.

• Better prioritization. Teams can evaluate alerts based on affected services, users, dependencies and business processes—not severity labels alone.

• More coordinated response. Shared workflows help security, IT operations, engineering, and network teams act from the same information.

• Stronger resilience. Organizations can reduce the likelihood that an issue escalates and restore normal operations faster when disruption occurs.

The business value extends beyond the SOC or network operations center. Observability can help leaders understand whether a technical problem is affecting customer experience, employee productivity, revenue, or regulatory obligations. Security context can help them assess whether that operational impact involves active risk. Together, those insights support better decisions about urgency, ownership, and investment.

Splunk's observability research found that 74% of respondents consider monitoring critical business processes at least moderately important to the business, while 65% said their observability practice positively affects revenue. This reinforces an important shift: observability is no longer only a troubleshooting capability. It is becoming a source of operational and business intelligence.

Five Best Practices Toward Connected Security and Observability

Organizations do not need to rebuild their entire operating model at once. A practical approach starts by improving visibility, adding context, and automating the workflows that create the most friction.

1. Establish Foundational Visibility

   Begin by identifying where critical telemetry is generated and where visibility gaps exist. Look across applications, infrastructure, networks, cloud services, endpoints, and identity systems. Determine which teams collect logs, metrics, and traces; which data is difficult to access; and where duplicated or inconsistent monitoring creates unnecessary cost.

   Prioritize the systems and business services where disruption would have the greatest impact. A clear service map can help teams connect underlying technical components to customer-facing and mission-critical processes.

2. Standardize and Connect Telemetry

   Data becomes more useful when teams can collect, organize, and interpret it consistently. Establishing common data practices across applications, infrastructure, networks, cloud, and security systems helps reduce blind spots and makes it easier to connect related activity.

   The goal is not to collect every available signal. Teams need the right data, enriched with meaningful context and retained appropriately, so security and operations teams can investigate issues, understand dependencies, and respond without manually reconstructing the story.

3. Add Context Before Adding Alerts

   More alerts rarely solve an information problem. Before expanding detection coverage, evaluate whether existing alerts contain enough context to support a decision.

   Can an analyst see the affected service, user, device, and dependency? Can the team distinguish expected behavior from an anomaly? Can they tell whether multiple alerts are part of the same incident? Can they assess potential business impact without opening several additional tools?

   Enrichment, correlation, and service context can turn isolated events into higher-confidence incidents. This improves alert hygiene and helps teams focus on meaningful activity instead of treating every signal as equally urgent.

4. Automate Repeatable Investigation and Response

   Identify the manual steps analysts perform most often and determine which can be standardized. Good candidates include collecting artifacts, analyzing suspicious files and URLs, enriching indicators, opening tickets, notifying stakeholders, and executing approved containment actions. 

   Automation should be designed around clear decision points, escalation paths, and human oversight. Start with repeatable, high-volume workflows where time savings and consistency can be measured. Then expand based on results rather than automating complexity for its own sake.

5. Measure Outcomes Across Teams
   
   Security and observability programs often use different metrics, but connected operations require shared measures of success. In addition to MTTD and MTTR, consider alert volume, false-positive rates, investigation effort, recurring incident categories, service availability, customer impact, and the percentage of incidents that require cross-team escalation. 

   Review these measures together. The goal is not to compare teams; it is to identify where fragmented data or handoffs are slowing down the organization. Shared metrics make it easier to prioritize improvements that reduce risk and operational burden at the same time.

Turning Splunk Intelligence Into Action with C1

Technology is only one part of connected security and observability. Organizations also need an architecture that fits their environment, integrations that preserve context and operating processes that teams can sustain.

Splunk provides a data foundation for security and observability across applications, infrastructure, and hybrid environments. Its security capabilities help teams centralize and analyze security data, improve detection, investigate threats, and orchestrate response. Its observability capabilities connect telemetry across the digital stack, so teams can understand service health, troubleshoot problems, and identify issues before they affect users.

C1 helps organizations translate those capabilities into operational outcomes. That can include assessing current tools and workflows, designing the architecture, deploying and integrating Splunk, connecting data across security and infrastructure domains, and optimizing the environment over time. C1 also brings managed security, SOC and threat-hunting capabilities, along with infrastructure, networking, and unified-observability expertise, that can extend internal resources and reduce ongoing operational burden., that can extend internal resources and reduce ongoing operational burden.

This combination is especially important for organizations that already own Splunk but are not capturing its full value. The platform may be collecting data, yet teams may still struggle with alert fatigue, disconnected workflows, incomplete coverage, or limited internal capacity. The next step is not always another tool. It may be better for data onboarding, stronger integrations, tuned detections, automated analysis, or a clearer operating model.

C1 meets organizations where they are. Rather than forcing a disruptive transformation, C1 can help stabilize current operations, optimize the Splunk environment, and progressively expand capabilities based on business priorities. The result is a more connected approach that improves visibility, accelerates response, and creates a clearer path to digital resilience.

Start with the Questions That Matter

Security and observability strategies should begin with the outcomes the organization needs—not the volume of data it can collect.

Ask:

• Can our teams see across applications, infrastructure, networks, cloud, and identity systems?

• Do security and operations teams use shared telemetry during investigations?

• Can analysts understand the business impact of an alert without switching between several tools?

• Which manual workflows consume the most time during detection, investigation, and response?

• Are we measuring alert quality and investigation effort, or only alert volume?

• Can we identify and respond to threats that resemble normal user or system activity?

• Are our Splunk investments aligned to the services and risks that matter most to the business?

The answers will reveal whether the greatest need is visibility, integration, automation, optimization, or additional operational support.

Security and observability are already connected by the incidents teams manage every day. The opportunity is to connect them intentionally—through shared data, consistent context, and coordinated action. When organizations do, they can spend less time assembling the story of what happened and more time protecting performance, users, and the business.