Adaptive Authentication Explained

Introduction to adaptive authentication

Traditional authentication methods—such as passwords and PINs—rely on static credentials, making them vulnerable to credential stuffing, phishing, and brute-force attacks. Because these methods are susceptible to brute-force attacks and user error, additional authentication factors (for example, one-time passwords from authenticator apps) and validations have emerged to address modern account-related threats. 

However, these security layers can introduce user friction and slow productivity. Static authentication policies fail to differentiate between low-risk and high-risk scenarios, leading to unnecessary security prompts or overlooked threats. Adaptive authentication is an alternative approach to user authentication that responds to user behavior across systems, adjusting authentication requirements according to risk and context. 

This article will look more deeply into adaptive authentication and how it can transform authentication for both users and an organization’s risk posture.

What is adaptive authentication?

Adaptive authentication, also called risk-based authentication, is a context-aware security approach that continuously evaluates authentication attempts and adjusts security measures dynamically based on real-time risk signals. Contextual factors—such as location, device, or time—related to a user's login or access request feed into adaptive authentication as part of the continuous risk assessment and evaluation process. 

Adaptive authentication is best understood by comparing it to traditional user authentication methods. Traditional authentication is binary—either granting or denying access based on credentials alone—without evaluating contextual risks. In contrast, adaptive authentication applies real-time risk assessment to determine whether to grant, challenge, or block access. Adaptive authentication monitors for threat signifiers, prompting additional verification or imposing stricter measures (such as blocking access) once a risk is identified.

Core adaptive authentication principles: Balancing usability and security 

Fundamentally, adaptive authentication aims to balance user convenience with continuous risk assessment and a strong organizational security posture. If you’ve ever had to use an authenticator app multiple times to complete a simple task, you probably have an intuition for when static authentication policies feel like overkill. Adaptive authentication exists to solve this problem without compromising on security where it matters. 

Through specific, definable rules, organizations can guide adaptive authentication assessment and leverage machine learning to analyze user behavior patterns and detect anomalies. The system identifies unusual patterns and responds dynamically to potential threats. This requires a balance between security and the user experience to minimize inconvenience for legitimate users performing their regular activities.

Common use cases

Adaptive authentication is dynamic by nature and ideal for scenarios including:

• Ensuring the security of internal systems and public-facing platforms.
• Balancing fraud prevention with user convenience.
• Mitigating risks in remote work and high-risk industries that rely on sensitive data, like finance or healthcare.

How adaptive authentication works

Adaptive authentication works by analyzing key factors of user behavior to assign a risk level to the user's request. Risk levels inform the system’s response, such as granting access to the user, requiring additional authentication like multi-factor authentication (MFA), or blocking access entirely. 

Key factors

The following risk signals inform an adaptive authentication system’s real-time security decisions, dynamically adjusting access policies based on contextual risk.: 

• User behavior: Monitoring how users interact with the systems, such as typing patterns, the frequency of their logins, and unusual login times.
• Contextual data: Analyzing the login attempt or request for access, such as the user's device, location, and IP address.
• Historical patterns: Cross-referencing with what the system already knows about the user, such as login history, in which a change in login habits can trigger a risk assessment.

Risk scoring and possible outcomes

Through post-analysis of the key factors above, the system assigns a risk score using machine learning algorithms that analyze user behaviors over time. This helps the system to categorize the user activity and make authentication decisions. The table below demonstrates an example of how adaptive authentication systems classify authentication requests and the decisions they make based on those classifications.

Benefits of adaptive authentication

User access remains one of the biggest cybersecurity concerns for organizations as they face sophisticated and dynamic challenges to securing the use of their technologies. Malicious actors can easily bypass traditional authentication methods by fooling users into sharing passwords or granting access, while others frustrated by cumbersome security protocols can tamper with them. In this section, we’ll explore the benefits of adaptive authentication in addressing these business challenges.

Improved security

Adaptive authentication identifies and disrupts identity-based attacks in real time, preventing adversaries from exploiting compromised credentials or bypassing MFA. Perceived risks are then addressed by dynamically adjusting the security measures based on the user's current risk profile, restricting access to authorized users only. Additionally, machine learning improves the detection rate of sophisticated attacks over time by continuously evaluating user behavior and contextual factors.

Enhanced user experience

Adaptive authentication is a dynamic approach, minimizing unnecessary friction during low-risk scenarios. Legitimate, low-risk system users can access resources without constantly encountering additional security hurdles. Adaptive authentication minimizes unnecessary MFA prompts, reducing MFA fatigue and enhancing user efficiency without compromising security.

Scalability

Distributed workforces and organizations supporting remote work benefit greatly, as adaptive authentication:

• Is device-agnostic: Capable of working across user devices, including desktops, laptops, or mobile devices.
• Adapts to user growth: Dynamically allocates resources to handle increased authentication requests as user numbers grow.
• Has global access management: Prompts additional verification depending on suspicious changes to user location.

Regulatory compliance

Adaptive authentication helps organizations meet regulatory compliance standards like GDPR, HIPAA, and PCI DSS through access control mechanisms, such as:

• User identity verification: The flexible and robust identity verification processes of adaptive authentication align with compliance frameworks that demand strict controls over data access.
• Enhanced security measures: Continuous monitoring, the implementation of MFA and risk-based assessments ensures that adaptive authentication can strengthen security protocols.
• Dynamic risk assessment: Continuous evaluation of user behavior and contextual factors allows organizations to apply appropriate authentication methods based on the perceived risk level.
• Facilitating audits and compliance reporting: Adaptive authentication systems can maintain extensive logs of access and authentication activities. This can simplify audit processes and help demonstrate adherence to regulatory requirements.

Challenges and limitations

Adaptive authentication offers a range of benefits, but there may be some challenges to implementation, depending on the needs and requirements of an organization.

Data privacy concerns

Adaptive authentication systems collect a range of data on users by design. This data can include user behavior, location, and device information. It's crucial for the organization to be aware of this data collection, to mitigate risks, and to commit to handling such data responsibly.

False positives and negatives

False positives and false negatives occur when a system incorrectly flags legitimate users as high-risk or grants unauthorized access when failing to identify a genuine threat. While false positives can disrupt user experience, machine learning models refine risk scoring over time, improving accuracy and reducing unnecessary security challenges. These risks can be mitigated by regularly reviewing and fine-tuning risk assessment rules and machine-learning algorithms.

Cost and complexity

While adaptive authentication requires an investment of resources, modern cloud-native solutions minimize deployment complexity by integrating seamlessly with existing IAM and security frameworks. Moreover, once deployed, organizations might need to hire experts or collaborate with third-party providers for support and train staff on how to use and manage it.

Leverage CrowdStrike for identity protection

Adaptive authentication is a critical component of a modern identity security strategy, preventing unauthorized access while optimizing user experience. As a context-aware and risk-based authentication system, adaptive authentication can dynamically adjust security requirements based on the perceived risk level for every login attempt. This offers your organization improved security and an enhanced user experience.

CrowdStrike Falcon® Identity Protection enhances adaptive authentication with real-time identity threat detection, risk-based access policies, and continuous session monitoring. By correlating identity, device, and threat telemetry, Falcon enforces dynamic authentication policies that block adversary access without disrupting legitimate users. With HYPR integration, you can enforce adaptive identity access policies based on device and identity posture, enhancing your organization's security through passwordless authentication.