Continuing our series on digital identity trust, we’ve tapped the expertise of Jason Keenaghan, director of offering management for IBM’s identity and access management (IAM) and fraud portfolio. In this episode, he’s diving into the challenge of accelerating growth without sacrificing security. How can organizations effectively identify and authenticate end users without introducing extra complexity or friction?
The Enterprise Growth Mindset
According to Keenaghan, it’s critical for companies to consider the end-to-end digital journey from initial user discovery to customer sign-up. Growth largely depends on removing friction, such as long registration forms and complicated authentication processes, while still maintaining the “assurance of identity.” This requires businesses to leverage provided data, such as email addresses and phone numbers, to corroborate with other sources and build up identity confidence. It’s also critical to conduct an in-app behavioral comparison to discover potentially malicious actors.
Common Digital Identity Pain Points
So where does it go wrong for many growth-minded enterprises? Keenaghan notes that passwords remain a problem: When customers encounter web and mobile difficulties they often leverage call centers, which are both expensive for companies and time-consuming for users.
Digital identity tool adoption is also an issue, with many enterprises using multiple consoles and software development kits (SDKs) to boost security. The result is increased complexity and reduced agility.
For Keenaghan, growth in a trust-native environment requires the combination of products, people and processes. Enterprises need integrated identity and access management (IAM) and endpoint management tools that reduce complexity, facilitate stakeholder alignment across desired outcomes, and provide key performance indicators (KPIs) and the necessary support to record and analyze critical indicators, such as abandonment rates and authenticator use.
AI Isn’t Just a Buzzword
Security leaders should also consider adopting artificial intelligence (AI) and machine learning tools. While Keenaghan notes that these are often used as IT buzzwords, he warns that attackers have begun using adversarial AI and social engineering tactics to bypass existing digital identity trust controls. As a result, companies need to adopt AI-driven behavioral biometrics and expert decisions systems that integrate with IAM solutions to both identify malicious bot activity and verify the identity of human users.
Companies can’t afford to sacrifice digital trust for agility or bypass growth for better security. Finding a balance requires customer-centric processes that are low-friction, highly adaptable, reliability delivered, and capable of leveraging AI and machine learning-driven technologies.
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