Financial institutions, and organizations more broadly, are increasingly investing in endpoint management as the volume and variety of endpoints grows.
That growth, coupled with stricter requirements that every endpoint accessing customer data be encrypted and patched, is part of why closer attention being paid to endpoint management.
The concept of the endpoint has evolved from laptops, desktops and mobile devices to encompass a range of Internet of Things devices, servers, cloud workloads, operational technology and artificial intelligence (AI)-enabled tools — an evolving attack surface that legacy endpoint programs weren’t designed to monitor.
“Most attackers understand that when a breach occurs, financial services institutions will often pay because downtime costs millions of dollars per hour,” says Apu Pavithran, CEO and founder of Hexnode. “The initial vectors for these breaches are almost always the endpoints, whether it’s a phishing email that drops malware or an unpatched vulnerability on a forgotten device.”
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Unified Endpoint Visibility and Patch Management
Attackers exploit the gaps that emerge when financial institutions’ visibility becomes fragmented across their diverse technology stacks.
“Compounding this, as AI accelerates both reconnaissance and social engineering, organizations are recognizing that inconsistent endpoint monitoring, manual processes and legacy tooling can no longer keep pace with modern threats — making unified visibility, continuous monitoring and effective response essential for reducing risk,” says Christopher Fielder, field CTO at Arctic Wolf.
Financial institutions can reduce their risk by incorporating a security-first approach into their daily workflows. That means every operational decision — from vendor onboarding to treasury operations and customer-facing services — should treat security as a standard component, Fielder says.
A practical first step is unified endpoint visibility, which eliminates the friction between an IT team that assumes a device that checked in yesterday is compliant and a security team that knows that device hasn’t been patched in three weeks, Pavithran says.
Those teams conflict most over patch management, but endpoint management solutions can establish automated patch policies with staged deployment groups.
“Test devices receive patches immediately for validation, and then you roll out to broader populations in waves based on device criticality,” Pavithran says. “Security gets fast deployment, and IT gets controlled rollout with visibility into what’s happening.”
Tool Integration and a Culture of Security
A more recent development is native integration of unified endpoint management dashboards, for tracking device health and compliance, and extended detection and response consoles, for monitoring threats and anomalies. Now, IT administrators can see security threat data, and security analysts can see device management context, without switching tools.
Financial institutions have the added challenge of not being able to just rip and replace core banking systems with outdated operating systems, so they must be realistic about the risk, using controls they can implement and working within acceptable technical debt, Pavithran says.
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These institutions can also elevate security awareness to an ongoing cultural competency with routine informational programs.
“To support this shift, CISOs and business leaders should promote a culture where employees feel comfortable spotting and questioning unusual requests,” Fielder says. “This approach to psychological safety directly reduces fraud risk and strengthens operational resilience.”
How AI Is Improving Endpoint Management
AI-led automation is improving endpoint management by enabling earlier detection, faster triage and more consistent security outcomes, but it’s not replacing human judgement.
Conversational querying allows IT administrators to ask in plain language for an instant list of all devices running an outdated OS, rather than clicking through multiple menus. AI also helps with troubleshooting, analyzing the context of failed actions and cross-referencing similar patterns to identify probable causes and remediations in real time.
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Reliably automating workflows means generating custom scripts for specific, personalized use cases, which AI can do in seconds after an IT admin describes the intent.
“The takeaway is that AI and automation only matter if they solve actual workflow problems,” Pavithran says. “Many organizations deploy sophisticated tools that remain unused because they fail to actually reduce ticket volume or administrative workload.”
