How Observability Improves IT and Security Workflows in Healthcare

Addressing IT Staff and Skills Shortages

With organizations short-staffed, they receive many alerts, which can lead to burnout. They may also struggle to keep pace with the complexity of modern IT environments. Health systems face a mismatch of skills and underinvestment in IT workforce development, Andriola says.

“Industries like healthcare, which must align compensation with tight operational budgets, often feel the pain more acutely,” Andriola says. “As a result, clinicians and patients depend on reliable, secure digital services, yet IT teams are often stretched thin, facing a constant stream of alerts, siloed tools and fragmented data — all of which contribute to burnout and inefficiencies.”

Pain Points in Healthcare IT Workflows

Healthcare organizations are overwhelmed with many scanning tools and configurations, Lobig says. Some processes are poorly defined, and silos hinder observability efforts.

“Many organizations often have multiple, disconnected monitoring and security tools that only provide a partial view of their environment,” Andriola says. “This increases silos and operational complexity and slows down issue resolution.” 

Andriola notes the dangers of downtime in healthcare: “In clinical settings, every second counts, and downtime isn’t just disruptive, it’s potentially dangerous. Whether in the ER, operating room or supporting personalized care, healthcare systems must operate in a real-time, always-on model.”

Observability can simplify complexity in organizations with interconnected digital services, Andriola says.

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How Observability Provides Visibility To Overcome Blind Spots

Observability tools provide an application topology to help organizations understand the interconnectivity of systems and network endpoints and map information flow. With the data that observability tools provide, organizations can quarantine vulnerabilities, Lobig suggests.

“In particular, the ability to understand information flows, service flows and quarantine applications and/or end points based on those vulnerability vectors is something observability tools can do,” he explains.

To alleviate the “blind spots” of traditional monitoring applications, observability tools provide a “single source of truth” and end-to-end visibility. They tighten governance across cloud, edge and hybrid environments, as well as integrate data platforms and large language model (LLM) workloads, Andriola says.

“More advanced observability platforms unify data pipelines, security telemetry and business analytics into a single stream, enabling consistent, trustworthy insights at scale,” he explains. “These tools help teams understand how data flows across silos and through analytical layers, building confidence in both operational and strategic decision-making.”

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Observability tools address silos by centralizing and streamlining data collection and analysis. These tools also correlate insights for organizations such as health systems. In healthcare, observability can minimize disruptions to critical systems for clinicians and improve the organization’s security posture, Andriola says.

Gaps in patient care can be life threatening, but full-stack observability can reduce disruptions for clinicians and IT staffers. Event consoles in observability tools allow organizations to maintain visibility and response while consolidating data from multiple systems.

“But visibility alone isn’t enough,” Andriola stresses. “In today’s environment, where operational volume and velocity are outpacing traditional IT models, only observability platforms that offer built-in intelligence and automation can scale with demand.”

Full-stack visibility makes IT staff more effective, he says: “By giving them real-time, full-stack visibility and clear context around performance or security issues, observability helps teams proactively optimize systems, resolve issues faster and focus their expertise where it matters most.”

How AI and Automation Aids Observability

Using AI in IT operations is a key strategy that allows organizations to automate fixes, reduce distractions and address issues proactively. It lets organizations create self-aware networks to prevent downtime and avoid financial loss.

“Today’s enterprises require an automation-first mindset to keep pace with emerging threats and rising complexity, especially in the face of persistent hiring challenges,” Andriola says. “Some advanced observability platforms, particularly those that integrate AIOps, help close the skills gap by reducing manual workloads and surfacing actionable insights in real time.”

AIOps can deliver fewer false alerts, faster root cause analysis and precise detection of vulnerabilities and anomalies, he explains.

“I think AIOps is an implicit functionality to observability,” Lobig says. “It’s really about using AI to make things more intelligent, like many vendors are doing in a variety of domains across everything technology related.”

AI models and agents will allow organizations to further develop their observability strategies. As observability tools evolve, IT leaders must develop new confidence in AI agents and LLMs, according to Andriola.

“Investing in unified observability is not just an IT decision,” he says. “It’s a strategic commitment to resilience, performance and business alignment in a world where digital operations are the operations.”

Going forward, observability must also address explainability and performance as organizations adopt LLMs and AI agents.

“Not all platforms offer this level of context-aware insight, which will become increasingly important in maintaining visibility into dynamic, intelligent systems,” Andriola says.