Sign up for the ITPro Today newsletter
Stay on top of the IT universe with commentary, news analysis, how-to's, and tips delivered to your inbox daily.
Insight and analysis on the information technology space from industry thought leaders.
Biometrics and AI Knock Out Passwords in the Security Battle
The integration of biometrics and AI is transforming authentication systems, offering advanced security, fraud detection, and user-friendly methods.
Industry Perspectives
December 13, 2024
5 Min Read
Alamy
By Nikhil Chandrashekar
As technology evolves, the future of authentication surpasses traditional passwords and relies on more secure and user-friendly methods, such as biometrics and artificial intelligence (AI). Biometrics, including fingerprints, facial recognition, and voice analysis, offer personalized security, while AI adds an additional layer of protection by continuously learning and adapting to user behavior. Together, these technologies promise to reduce the risks associated with password breaches and enhance the overall authentication experience.
Why Is Enhanced Security Necessary?
According to a report from the Institute of Security + Technology, using "something you know," such as passwords, PINs, or answers to security questions, has always been flawed due to their reuse, countless data breaches, and ease of using computers to test many combinations to guess a password. These methods are "even less reliable due to AI's ability to summarize and recall information from large datasets, to include scrapes of public social media, public records, and pooled data breach takings." The researchers say that generative AI has the capability to fool some biometric authentication methods, which puts the reliability of "something you are" at risk.
Benefits of Using Biometrics and AI
Biometrics and AI work synergistically to enhance authentication in several ways, including:
Improved accuracy through AI algorithms. AI-driven algorithms can process biometric data such as fingerprints, facial features, voice, or iris scans with higher precision and accuracy than traditional methods.
Enhanced fraud detection. AI excels at recognizing patterns, which allows it to detect anomalies in biometric data that might indicate fraud. AI can also use behavioral biometrics — such as how a person types or moves their mouse — to provide ongoing, passive authentication, ensuring that the person using the system remains authenticated.
Adaptive learning and continuous authentication. AI enables biometric systems to improve over time by using machine learning models that adapt to the user; the system can continuously learn from the user's behavioral and physical patterns, adjusting to changes in real time.
Multimodal biometric systems. AI is fundamental to the integration of multiple forms of biometric data (e.g., face, fingerprint, and voice) that are combined for more secure authentication.
Real-World Examples of Biometrics and AI-Powered Authentication Success
Biometrics and AI-powered authentication have moved beyond concept to successful application. For instance, HSBC's Voice ID voice identification technology analyzes over 100 characteristics of an individual's voice, maintains a sample of the customer's voice, and compares it to the caller's voice. The voice-biometrics system cut the institution's telephone banking fraud in half, identifying more than 43,000 fraudulent phone calls and protected £981 million of customer assets.
In India, Aadhaar, a biometric-based identity program designed to curb corruption, has saved about USD $9 billion by eliminating fraud in the financial distribution system. Microsoft joined the FIDO Alliance and other major platforms in 2022 to support passkeys, a common passwordless sign-in method. The tech giant alone blocked 70 billion email and identity attacks that year.
Consumer Concerns About Biometric/AI-Powered Authentications and Possible Solutions
Despite successes in increasing security, there are concerns and potential solutions:
Privacy and data breaches. A 2019 massive data breach involving the biometric security platform BioStar2 exposed 27.8 million sensitive biometric records. To reduce the chance of these breaches, companies can identify weak spots in their security infrastructure and implement appropriate intrusion prevention measures, educate employees on security policies and contingency plans, and practice network segmentation and data categorization.
Bias and accuracy. In addition to algorithms performing differently, the demographics of the people whose faces are put through facial recognition programs can impact results, according to a Georgetown Law Center on Policy & Technology report. For instance, the report found that misidentifications were higher for subjects from West and East Africa and East Asia than for Eastern European subjects, as well as for women, the elderly, and children. These results underscore the need for diverse training data and ongoing algorithm assessment. Still, biometric identification is not an infallible system. "The stakes of misidentification could be, depending on the context, really high, and inequalities that already exist could be exacerbated," one expert says.
Best Practices for Integrating Biometrics/AI into Existing Authentication Systems
The success of implementing biometrics and AI into existing systems relies on organizations to follow best practices. Organizational leaders can assess organizational needs by conducting a security audit to identify vulnerabilities that biometrics and AI can address. This information is then used to create a roadmap for implementation considering budget, resources, and timelines. Involving appropriate staff in such discussions is essential so all stakeholders understand the factors considered in decision-making. Selecting the right technology calls for careful vendor evaluation and identification of solutions that align with the organization's requirements and compliance obligations.
Once these decisions are solidified, it is prudent to use pilot programs to start the integration. Small-scale deployments test effectiveness and address any unforeseen issues before large-scale implementation.
Educating staff on new systems ensures seamless adoption and highlights the importance of security practices. Some of these best practices include:
Multifactor authentication. Combine biometrics with additional authentication factors for enhanced security.
Privacy by design. Incorporate privacy considerations from the outset, following industry-accepted frameworks.
Regular updates and monitoring. Keep AI models and security systems updated to protect against emerging threats.
Risk mitigation. Employ advanced encryption methods to store and transmit biometric data following regulations and industry-specific standards.
Continuous learning. Stay informed by subscribing to industry journals, upskilling through training and certification programs, and participating in cybersecurity events and forums.
Engage with experts. Consult with cybersecurity professionals to stay abreast of best practices and technological advancements.
As biometrics and AI shape the next generation of authentication systems, companies and users are expected to embrace more innovative solutions. Organizations that prioritize more secure environments and improve user confidence in digital interactions will thrive, while those that don't risk being left behind.
About the Author:
Nikhil Chandrashekar is a senior programmer analyst for Droisys. A recognized leader in backend engineering and data security, he has more than a decade of experience driving innovative, secure software solutions across varied industries. Nikhil excels in architecting secure systems, implementing advanced authentication protocols, and mentoring teams on best practices. He holds a master's degree in computer science from San Diego State University. Connect with Nikhil on LinkedIn.
About the Author
You May Also Like
.jpg?width=700&auto=webp&quality=80&disable=upscale)
