IN the digital age, customers and employees expect uninterrupted access to services and applications at all times. On the flip side, organizations are also keeping a close eye on the implications of privacy and compliance.
How are they related? Apps and services rely heavily on data being shared across platforms.
With the implementation date of GDPR looming closer, companies are scrambling to find a balance between security and providing a seamless customer experience.
To help make administration easier, organizations often use Customer Identity and Access Management (customer IAM) solutions to help manage customer identity and profile data. This means organizations can easily control who has access to certain applications and services.
However, not all customer IAMs are adequate in providing the necessary protection. Traditionally, customer IAMs are built based on enterprise IAM solutions, which often employ many security gateways.
Customer IAMs are different. Most customers wouldn’t put up with troublesome authentication processes. This means sometimes, customer IAM solutions are user-friendly, but at the risk of less robust security measures.
This is no longer an option. Recent years have seen more high-profile attacks, each increasingly sophisticated than the last. Two-factor authentication is no longer sufficient in protecting against malware-based “man in the browser” or identity theft attacks.
Customer IAMs requires a new approach – and machine learning (ML) can help. ML provides continuous authentication through learning a customer’s usage patterns. Increasingly, customer IAM vendors are using improved analytics, right from the registration stage, to build a profile by gathering data at various touchpoints.
Behavior analytics with the use of ML can help identify suspicious activity logged by certain users. This is far more effective than passwords, as attackers have a much harder time mimicking patterns in click and typing behaviors than cracking codes.
This also makes it more important for organizations to ensure compliance with data protection regulations like GDPR. Behavior-based analytics are heavy on data, and the organizations implementing g these solutions need to ensure user consent tools are clear and simple to understand.
A customer IAM should be built with the customers in mind, but not at the expense of security. It is paramount for the workflow to have built-in anti-fraud defenses, without affecting customer experience.
For organizations, achieving that balance between security and customer experience is definitely achievable, you just need to make sure you choose the right customer IAM for your business’s needs.