This is the final installment in a three-part series. Be sure to read part 1 for the more information on unified endpoint management (UEM) and part 2 for additional details surrounding the Internet of Things (IoT).

Whether you read about it in a news article, saw it on a TV commercial or demoed it at a technology conference, you’ve probably had at least one interaction with cognitive technology as it relates to modern business. If you’re ahead of the curve, you’ve already begun using it daily.

Since cognitive UEM is such a new concept, very few professionals have the tacit knowledge or years of experience required to determine how effective use cases are. Before everyone begins exclaiming that they’re taking a cognitive approach to solve their biggest challenges, let’s first take the time to examine what constitutes cognitive computing to better understand how it’s changing the way we work.

Watch the on-demand Webinar With Forrester Research

What Does It Mean to Be Cognitive?

The Oxford English Dictionary defines cognition as “the mental action or process of acquiring knowledge and understanding through thought, experience and the senses.” The term has evolved in the context of modern-day business as artificial intelligence (AI) became integral for behavior modeling, data crunching and predictive analytics. Cognitive systems can analyze massive amounts of data generated through endpoints such as smartphones, tablets, laptops, desktops and IoT devices. These systems offer diagnostic, predictive and prescriptive analytics tools that observe, learn and offer insights.

Unified endpoint management is mission-critical for enterprises seeking to achieve business transformation heading into the next decade. Although all UEM tools simplify day-to-day processes for IT administrators by bringing everything under one roof, the process of assimilating data gathered across various types of endpoints has become no less complicated.

With more devices in use than ever before, and an expected explosion in the number of endpoints with the rise of the IoT, data analysis poses significant challenges for those looking to achieve effective management and security. Cognitive UEM solutions can help analysts make sense of the massive amounts of data generated by endpoints and their users, apps and content.

According to the new thought leadership paper, “Mobile Vision 2020,” a commissioned study conducted by Forrester Consulting on behalf of IBM, “the IT and security pros surveyed recognize the value of applying AI/cognitive computing to their organizations’ endpoint data, with the majority indicating it plays a very important or critical role in their endpoint management (76 percent) and endpoint security (83 percent) strategies.”

A Day in the Life Without Cognitive UEM

Perhaps the best way to recognize the value of cognitive technology is to venture into a day in the life of a UEM administrator without it. Tasked with managing multitudes of endpoints across all operating systems and form factors, IT professionals must continually read up on information about multiple operating systems, millions of apps, and looming threats and vulnerabilities.

With this information, analysts must identify opportunities and threats that can improve or potentially hinder productivity and the overall user experience. Combined, there are over 3 million public apps available in the Apple App Store and Google Play that can be assessed for productivity benefits. By the same token, there are millions of apps unknown to these app stores that could potentially infect applicable endpoints. There is a significant amount of structured and unstructured data — internal and external to corporate environments — that, if left undiscovered, could hinder an organization’s performance and reputation.

Now imagine that you work for a global organization with over 5,000 employees accessing corporate data through their laptops, smartphones and tablets, running countless applications and different operating systems. The amount of data generated through connected devices in this scenario is to the tune of 100,000 events per day alone. Multiplexing these events across millions of apps, multiple endpoint management policies, and ever-evolving threats and vulnerabilities, the diagnoses and prevention of incoming threats is of critical importance. Ensuring a risk-free security posture, as well as leveraging the latest and greatest UEM and OS capabilities within the ever-evolving management paradigms, suddenly seems like a tedious task.

So how do UEM administrators discover, diagnose, predict and prevent risk exposures, and identify the impact of opportunities within their environment? According to the Forrester paper, “over 80 percent will implement AI/cognitive computing by 2020 to analyze the vast — and increasingly growing — volume of endpoint data they collect.”

Enabling Endpoints, End Users and Everything in Between

Cognitive computing and AI support two distinct features that help the UEM administrator. The first is natural language processing (NLP), which is the capture and identification of data. NLP parses the datasets and identifies relevant information that could potentially impact target entities, such as devices and their apps. The second key feature is machine learning, the ability to see data patterns and predict outcomes by analyzing historical patterns or through training on relevant data.

Leveraging these distinct features, cognitive computing correlates external sources, including data on active threats such as zero-day vulnerabilities and malware, the latest updates to operating systems, social media posts on Twitter, blogs and analyst reports to identify relevant insights by analyzing the near real-time posture of endpoint devices.

With cognitive UEM, IT professionals can mitigate risks while jumping on opportunities that improve end-user productivity, with minimal business impact and at a reduced cost. The Forrester study noted that “investment in AI/cognitive computing will increase by greater than 300 percent in 2017 compared with 2016.”

Elevating Your Security Strategy Before 2020

Now that you know what it’s like to manage an endpoint and mobile environment with and without cognitive UEM, which approach will you choose? No matter what path you take, bear in mind that this is an evolving field and we will see regular progress as we move ahead.

Watch our on-demand webinar, “Forrester Forecasts 2020: Is Your Mobile Strategy Aligned?” to learn more about the biggest trends heading into the next decade: UEM, IoT and cognitive/AI. Wes Gyure, IBM MaaS360 portfolio offering manager, and special guest Chris Sherman, senior analyst at Forrester, share top considerations for each as you craft your transformational strategy.

More from Artificial Intelligence

X-Force releases detection & response framework for managed file transfer software

5 min read - How AI can help defenders scale detection guidance for enterprise software tools If we look back at mass exploitation events that shook the security industry like Log4j, Atlassian, and Microsoft Exchange when these solutions were actively being exploited by attackers, the exploits may have been associated with a different CVE, but the detection and response guidance being released by the various security vendors had many similarities (e.g., Log4shell vs. Log4j2 vs. MOVEit vs. Spring4Shell vs. Microsoft Exchange vs. ProxyShell vs.…

Unmasking hypnotized AI: The hidden risks of large language models

11 min read - The emergence of Large Language Models (LLMs) is redefining how cybersecurity teams and cybercriminals operate. As security teams leverage the capabilities of generative AI to bring more simplicity and speed into their operations, it's important we recognize that cybercriminals are seeking the same benefits. LLMs are a new type of attack surface poised to make certain types of attacks easier, more cost-effective, and even more persistent. In a bid to explore security risks posed by these innovations, we attempted to…

Artificial intelligence threats in identity management

4 min read - The 2023 Identity Security Threat Landscape Report from CyberArk identified some valuable insights. 2,300 security professionals surveyed responded with some sobering figures: 68% are concerned about insider threats from employee layoffs and churn 99% expect some type of identity compromise driven by financial cutbacks, geopolitical factors, cloud applications and hybrid work environments 74% are concerned about confidential data loss through employees, ex-employees and third-party vendors. Additionally, many feel digital identity proliferation is on the rise and the attack surface is…

AI reduces data breach lifecycles and costs

3 min read - The cybersecurity tools you implement can make a difference in the financial future of your business. According to the 2023 IBM Cost of a Data Breach report, organizations using security AI and automation incurred fewer data breach costs compared to businesses not using AI-based cybersecurity tools. The report found that the more an organization uses the tools, the greater the benefits reaped. Organizations that extensively used AI and security automation saw an average cost of a data breach of $3.60…