Employees transitioning to remote work environments en masse have caused what IT teams feared most: a spike in security issues as use of devices with various degrees of adherence to security protocols increased. A survey of 1,600 IT professionals revealed 66% have experienced more security issues due to remote work. The top threats reported include malicious emails (58%), risky, non-compliant employee behavior (45%) and an increase in software vulnerabilities (31%). Systems that previously ran smoothly, aided by asset visibility and regular software patching, are now exposed to both external and insider threat behavior through devices and applications that are not thoroughly vetted for use on a corporate network.
All these threats have increased IT workloads by a daunting 63%, according to the survey. IT teams, short-staffed and facing hiring freezes because of the pandemic, need a new approach to tightening controls in a remote environment that doesn’t require additional staff. Hyperautomation can help IT reduce new help desk tickets and widen the security net over myriad remote devices. In other words, shifting from manually intensive tasks to automated processes will accelerate remediation of issues before they can disrupt the network and, through machine learning, provide greater visibility into devices and behavior that may indicate a security threat.
Putting Hyperautomation to Work
Hyperautomation tools can resolve up to 80% of issues before users even report them. IT can reduce help desk and service management workloads by using high-speed automation, AI, machine learning and predictive analytics to proactively remediate issues – gaining greater intelligence into the types of threats the business faces and related behavioral patterns. These advanced tools also lessen the burden of remote work for IT, which can use hyperautomation to become more proactive in security practices. Remote workers, too, benefit from continued productivity without disruptions caused by successful threats.
Further use of automation, AI and machine learning can help IT more effectively manage a more secure remote environment in the following ways:
Enhanced Threat Insight. The first line of defense is being able to see, across your entire environment, what applications, devices and software are in use. Hyperautomation tools can sense what is in the environment, analyze vulnerabilities and monitor any changes in usage or device behavior. Continuous sensing, discovering and detecting security issues enables IT to prioritize remediation of critical issues, further protecting the network from threats.
Better Edge Visibility. IT can query all edge devices using natural language processing (NLP) and get real-time intelligence across the enterprise in seconds. It provides quick operational awareness, real-time inventory and security configurations across the edge by leveraging sensor-based architecture.
Risk-Based Prioritization. One of the primary means of protecting the network is the diligent execution of patching updates. Using machine learning and predictive analytics, IT can learn what is being actively exploited so risk response is performed based on threat priorities. Patch reliability data can be automatically delivered with actionable intelligence pulled from thousands of public and crowdsourced sentiment data.
Faster Patching. Machine learning can also help reduce time-to-patch, a critical concern in addressing threats effectively. Intelligence data can provide information on patch reliability, so security teams can act on threats faster. IT security teams know that threat actors love patching delays. The median time for a threat actor to develop a functional exploit is 22 days. Bluekeep was a publicly visible example of how quickly an exploit can be engineered. Within 14 days of the update’s release in May 2019, six independent research teams had created and demonstrated functional exploits of the Bluekeep vulnerability. Getting ahead of that development cycle is critical for IT operations to stay ahead of threats.
Proactive Remediation. Automated device and application use monitoring can identify vulnerabilities, prioritize risk and then remediate those posing the most immediate threat. Automated remediation enables IT operations to move from a reactive response mode to a proactive, ‘self-secure’ model, addressing vulnerabilities before threat actors break through. A recommended standard is a 14-day service level agreement (SLA) for vulnerability remediation.
From Concerns to Control
Researchers estimate that 25-30% of the workforce will continue working at home, multiple days a week, by the end of 2021. Remote workers are increasingly comfortable with this practice and will continue to do so, post-pandemic.
Security remains a chief concern for organizations anticipating this long-term remote work trend. Hyperautomation offers a solution to addressing myriad security concerns; specifically, automated remediation and prioritizing high-risk threat intervention can help neutralize the main vulnerabilities in remote work. Moving to a self-secure model can stop threats from software vulnerabilities and threats generated by risky employee behavior, like phishing. By using AI and machine learning, IT can identify anomalies before they impact the network.
Remote work has been a wake-up call for many organizations still in the early phases of AI and automation adoption. The rapid growth and complexity of devices, data, multigenerational remote workforce and increasing cybersecurity threats are driving organizations to take a serious look at hyperautomation. The capabilities of advanced automation, like machine learning and predictive analytics, can help take the worry out of remote work and, better yet, help IT be more effective in mitigating security risk.