The Internet of Things’ rapid growth and ubiquitous connectivity drive companies’ need to secure their intellectual property and consumers’ personal identifiable information—particularly to meet strict regulatory requirements, such as those in the banking and financial industry. Many companies use conventional identity verification methods; however, cybercriminals can steal or hack these credentials easily, leaving privileged data vulnerable.
The shift away from traditional authentication processes alone to using more sophisticated credentials has already begun. Frost & Sullivan’s research team notes that the growing use of biometrics—such as fingerprint, face, iris, voice, and behavioral patterns—will provide a more secure and seamless identity verification experience than traditional credentialing procedures. Organizations cannot rely solely on one authentication vector, requiring them to utilize legacy and newer credentials together in multi-factor authentication processes.
For financial institutions or organizations considering how to utilize a more advanced, multi-factor security capability, Frost & Sullivan’s analyst team has assembled its Top 5 Best Practices for risk-based authentication solutions:
- Brand-Agnostic Technologies: Risk-based authentication solutions must be technology- and credential-agnostic, enabling companies to use any existing solution along with new technology purchases. By avoiding rip-and-replace strategies while harnessing new, modern technologies, organizations can increase their security posture and still meet consumer demand.
- Behavioral Monitoring and Analytics: Organizations must utilize an authentication solution that contains behavior monitoring and analytics capabilities assisted by machine learning to verify a user’s identity continuously throughout each session. Such solutions recognize a user’s habits and normal patterns and require further authentication in the event the risk engine detects suspicious behavior.
- Fraud Analytics: Authentication solutions equipped with these capabilities prevent the unauthorized transfer of funds and other malicious activities by cybercriminals. If a transaction is suspicious—abnormal time, unusual location, or high dollar amount—the solution requires additional credentials to be presented as verification, rather than rejecting the transaction or holding it for manual review. Transactions can then only proceed once verified credentials are presented; if not, the process is cancelled.
- Machine Learning Capabilities: By utilizing machine learning capabilities, security systems can learn to detect suspicious activities and behaviors in real time—a key capability to halt malicious cyber activity. Risk-based authentication solutions integrated with machine learning capabilities are essential to maintaining situational awareness and a healthy security posture.
- Mobile Security Applications: With widespread app usage and more complex transactions occurring through mobile devices and networks, organizations’ threat vectors have exponentially increased. Key-logging malware, hacking through unsecured wireless networks, and mobile-targeted malware types are attractive to hackers seeking richer user, transaction, and operating system data, requiring financial organizations to seek solutions that can protect these new threat vectors.
Conclusion
Frost & Sullivan’s research team finds OneSpan meets these best practices, enabling the company’s clients to achieve a strong security posture via best-in-class risk-based authentication processes. Risk-based authentication appeals to consumer demand for more robust security protections without sacrificing mobile access and user experience while enabling financial institutions to ensure that the transactions, consumer data, and organizational data remain safeguarded from malicious attackers.
