Security Challenges and the Future of the IAM Market

Businesses conducting risk reviews can’t neglect cybersecurity in their assessments. The digital landscape is always changing, and projections suggest identity and access management (IAM), cloud services and updated security models will be key considerations for 2020.

Projections Show Rapid IAM Market Growth

Global market value for identity and access management is expected to hit $24.52 billion by 2025, up from $10.41 billion in 2018. Driven by expanding cloud adoption, the increased popularity of online banking and the introduction of more authentication methods, growth will continue at a compound annual rate of 13.02% across the market as a whole. Some segments, such as provisioning and multi-factor authentication (MFA), may grow faster or represent greater market share due to an increasing demand for specific products and services.

Cybersecurity Experts Face New Challenges from Innovative Hackers

Businesses may be surprised to discover they’re not so different from today’s hackers in the way they plan and execute their tactics. Far from being a bunch of enterprising but disorganized criminals without clear goals, hackers are engaging in global endeavors across the dark web, strategizing and competing in the same ways as legitimate corporations.

Trade in credentials, including credit card numbers, driver’s licenses and passports, has become as simple as e-commerce shopping. Stolen information can be purchased as individual records or in bulk batches and used for the purpose of identity theft, network infiltration or wide-reaching malicious attacks on numerous organizations.

Because identifying information is so readily available, data breaches must be treated as “when” instead of “if” possibilities. Cybersecurity experts and businesses need to understand hackers are formidable opponents and respond by putting stronger defenses in place to guard against unauthorized network access.

Zero Trust is Becoming More Nuanced

The zero trust model, defined by CSO as “a security concept centered on the belief that [an organization] should not automatically trust anything inside or outside its perimeters,” requires verification for “everything trying to connect” to a system. Access is denied anytime verification fails. More networks are adopting this model to guard against malicious access and prevent breaches caused by poor third-party security.

To succeed, a zero trust structure must take into account the unique combinations of users, behaviors, devices and access needs both inside and outside of networks. It’s not enough to consider only users directly associated with a network. Businesses must look beyond internal access and assess the security protocols of companies providing “as-a-service” products, such as software and identity management, as well as vendors, partners and other third parties connecting to internal systems. Loopholes and vulnerabilities in any area can lead to onsite network compromise even in zero trust environments.

Performing security audits and identifying the greatest threats provides a framework for zero trust implementation and management, and developing ongoing training for employees minimizes the risk of internal compromise due to ignorance or error.

Evolution of Cloud Computing Environments Requires More Focus on Security

North American businesses rely heavily on cloud environments for daily operations. Sixty-six percent have private internal clouds in place, and 65% use public cloud infrastructure. These complex cloud environments often include combinations of modern and legacy applications requiring nuanced access management to guard against attacks.

With increased cloud adoption comes more data, which attracts the attention of hackers. Enterprises and cloud providers must both assess internal security and access protocols, identify areas of weakness and deploy updated strategies designed for today’s evolving network structures.

Over 1 Million Customers Affected by T-Mobile Data Breach

News of the latest data breach at T-Mobile serves to emphasize the necessity of strong security and strategic IAM protocols. More than 1 million prepaid data customers were affected by the breach, which exposed several categories of personal information, including:

• Names
• Billing addresses
• Phone numbers
• Account numbers
• Plan information

Due to the nature of the affected data, T-Mobile was required to alert all affected customers. The company has since shut down access to the compromised database but hasn’t yet reported how long the information remained open to unauthorized access.

No passwords were stolen, but it’s possible for hackers to use the exposed identifying information to attempt to impersonate T-Mobile customers and gain access to accounts. The concern is nothing new, since the carrier previously suffered a similar breach affecting 3% of its customers in August of 2018.

Identity and access management certifications

The time has come for businesses and cybersecurity experts to prepare for greater threats and begin adopting protocols to safeguard against the strategies of modern hackers. As 2020 approaches, IT teams must focus on shoring up cybersecurity defenses and leveraging new tools for data protection. Attacks may be inevitable in the modern digital landscape, but a calculated approach to security offers the protection modern enterprise networks need to stay protected.

Preventing Identity Theft With Machine Learning

Stealthy hackers and targeted attacks are making it difficult to detect threats to users’ identities, especially in growing enterprise networks. More users and devices contribute to an increase in data, which must be monitored and analyzed for risks and potential breach activities.

The 79% increase in account takeovers from 2017 to 2018 points to overburdened IT departments lacking the resources to handle the monumental task of combing through data for malicious actions and responding to attacks upon discovery. Machine learning (ML) provides powerful tools to help with threat monitoring and detection and increase protection for all network users.

Learning and Determining Risk Levels

To “learn” what breach activity looks like, ML systems must be taught using either supervised or unsupervised learning methods. In supervised learning, ML tools are presented with known data sets, such as user behaviors, tagged as normal or aberrant. This establishes a statistical model the system later uses to differentiate between standard user activities and signs of network infiltration. The IT teams can adjust ML algorithms to correct false positives and improve future performance.

Unsupervised learning occurs when an ML system draws on known information about a person or group of people performing actions or making access requests on the network. Systems can then determine whether to approve or deny requests based on users’ privilege levels and access requirements. After initial “training,” ML is able to continue to learn new patterns and behaviors. Known as self-learning, this process enables classification of actions according to risk level to detect hacker infiltration without human intervention.

Protecting Users and Devices

The users accessing enterprise networks may be employees, vendors, suppliers or customers and may interact with data using a variety of devices. Businesses must address vulnerabilities and security loopholes to safeguard sensitive data and prevent network infiltration.

By training ML systems to understand and differentiate between varied types of user behaviors, enterprises can implement safeguards to be deployed automatically when malicious activities are detected. This minimizes the risk of fraudulent transactions and saves businesses the hassle and expense of cancellations and refunds.

Machine learning algorithms can also detect threats on devices while devices are in use, which prevents users from unknowingly infecting networks with malware from compromised devices and locks out hackers attempting to use stolen devices to gain access to network data.

Authentication and Fraud Detection in Real Time

The best IT department could spend every waking moment analyzing network activity and still fail to catch subtle attempts at identity theft or hackers operating with stolen credentials. Incorporating ML enables security systems to consistently monitor data sets and behaviors while learning and updating in response to new information.

Because ML operates in real time, problems are flagged at the moment of discovery. Alerts can then be passed on to the appropriate people in the IT department, or a predetermined solution can be deployed to prevent network compromise. Continual assessment of behaviors and risk levels supports smarter approval and denial of access requests, thus minimizing false positives and allowing IT departments to address real threats before user data is compromised.

Building to Scale

Humans can only handle so much data before requiring help, and with the massive scale of information collection and analysis at the enterprise level, it’s not practical to continually expand the IT department in an attempt to keep up with the influx. Even small businesses deal with a significant amount of data and benefit from the assistance of automated systems.

When using ML, more data is a help rather than a hindrance. No matter how many users and devices are introduced into the network, a security system with ML can continue to learn new sets of patterns and behaviors. Increased detail refines the system over time and reduces unnecessary security alerts. Businesses are free to diversify network access without risking compromise or outpacing the system’s ability to monitor network use.

Advancing Cybersecurity

Cybersecurity experts use ML to delve deep into the dark web and gather information to inform businesses of potential breach activities in advance. Just as ML can monitor enterprise network activity, it can also collect data from across the numerous channels hackers use to communicate and do business. Activities can be analyzed for potential threats, such as sales of detailed identity information or transfers of malicious files. Cybersecurity experts either use this information to enable the companies for which they work to protect their networks in advance or provide the results of data analysis to allow enterprises to improve onsite threat detection and response.

Identity and access management certifications

Making ML a primary tool in identity theft prevention helps safeguard businesses against inevitable attacks and preserve the identities of all users with network access. In combination with a qualified team of IT professionals trained in identity protection, ML supports a safe network environment and protects sensitive business data from clandestine threats.

Reasons to Upgrade IAM Systems

Companies must continuously assess and upgrade IAM systems to manage risks. Operating with outdated identity and access management (IAM) systems limits business operations and puts networks at a higher risk for data breaches. In light of changing access needs and the complexity of modern threats, IT teams must examine existing systems for signs of obsolescence and take steps to implement solutions with features designed to support modern access needs.

In light of changing access needs and the complexity of modern threats, IT teams must examine and upgrade existing IAM systems.

These six signs are clear indicators and reasons to upgrade IAM systems:

A Legacy System is No Longer Supported

When a system reaches end of life, support dries up and updates cease. This can cause serious problems for businesses as vulnerabilities multiply and leave networks open to attack. IT teams can quickly become tied up troubleshooting problems without help from the system’s manufacturer, and performance will eventually fall short of IAM requirements. It may be impossible to successfully integrate new applications or devices, which has a negative impact on productivity and growth. New technologies introduced in the IT environment are left without protection or must be managed using a separate IAM solution. The resulting silos limit visibility and put IT teams in the awkward position of having to monitor two access environments simultaneously.

Third-Party Access Requirements are Increasing

Allowing increasing number of users to access a business network inherently creates a greater breach risk, which is exactly what IAM is meant to address. However, third-party access by customers, vendors, suppliers and other outside entities introduces additional concerns in self-contained legacy systems. Without tools to extend IAM controls beyond the confines of the main network, a business inherits every vulnerability of its partners.

To properly monitor the third parties accessing business systems and deploy appropriate controls in response to changing risk levels, companies must upgrade to modern IAM solutions. Granular control with automated provisioning and deprovisioning is essential for mitigating breach risk while maintaining appropriate access levels.

Automation is Limited

No IT team has the time or the resources to monitor every action taken on a business network. Human and device identities make a massive number of access requests every day in a typical enterprise, and hackers can easily slip under the radar if automated monitoring tools aren’t in place. A lack of agility and adaptability in permissions increases the burden on IT departments, requiring staff members to handle application authorizations, integration requests, provisioning and deprovisioning. Finding and addressing dead or orphaned accounts also falls to the IT team, and the process can take a significant amount of effort if a company has recently undergone a change in staffing.

Handling these responsibilities leaves little time to evaluate risk levels, address security alerts and launch protective measures against potential breach activity. Today’s IT professionals need the help of automated IAM systems with intelligent monitoring and controls to ensure the highest level of protection.

Scale is Becoming a Problem

The growth every business owner works for can become a nightmare if it outpaces the capabilities of a legacy IAM system. Limitations restrict functionality, and a complete upgrade may be the only choice for expansion. Failing to implement a new solution can lead to sluggish logins and slow system responses, which can spark frustration among users and drive down productivity.

Legacy systems may also put a cap on the number of applications a business can deploy. As competitors adopt newer and more powerful solutions, companies relying on outdated IAM platforms run the risk of falling behind. Restricted accessibility may also minimize options for mobile and remote workers and put limits on employees who desire flexibility.

Compliance is Threatened

Modern privacy regulations demand detailed network audit information and strong security protocols to keep data safe. Companies in industries with strict laws dictating the protection of highly sensitive information, such as health records or financial data, need IAM solutions with detailed monitoring and reporting tools. Adding a custom solution to an existing system in the interest of remaining compliant takes undue time and resources and puts more stress on overworked IT teams.

Newer Technology is Available to Upgrade IAM Systems

Holding onto legacy systems makes little sense when numerous cloud IAM solutions are available for businesses of all sizes. Artificial intelligence and machine learning make these tools faster, smarter and more adaptable than legacy IAM, which allows businesses to fine-tune access control policies for better management of all identities. Options like self-service password resets offload some of the burden from the IT department by putting minor administrative tasks in users’ hands, and improved authentication increases security across platforms and applications.

Identity and access management certifications

Updating IAM systems isn’t a task to be put on the back burner. IT teams should be consistently evaluating current IAM tools and solutions for shortcomings, flaws and vulnerabilities and making recommendations to strengthen network security. Creating a more agile system protects data in a changing threat landscape and allows businesses to adapt with ease as new threats appear.