Can machine learning balance effective security with a good UX?
AT FIRST GLANCE, security and user experience (UX) might seem like mutually exclusive concepts.
Stringent forms of security consume more time on the user’s end and make it inconvenient for users who tend to go looking for alternatives or complain about the lack of productivity.
In fact, there are times that businesses skip security measures just to ensure the UX is preserved and delights users. After all, systems don’t need protection from the actual user.
However, under such circumstances, business data becomes quite vulnerable, and stakeholders find it hard to absorb the risk.
With breaches costing a global average of US$3.86 million, it’s high time businesses find a balance between UX and security — and if experts are to be believed, machine learning (ML) holds the key to the problem.
The self-learning technology can adapt to each user’s actions and behaviors without compromising security parameters as ML algorithms leverage contextual intelligence to continually adapt.
This means that the technology can adapt to the risk context and assess risk scores in real-time.
With that in mind, accessibility will be more seamless at each login as the reauthentication process will be shorter. Naturally, this translates to a better UX as users can access their accounts more easily.
In the event of a breach, ML’s big brother — artificial intelligence (AI) — automatically triggers a sequence to disable access to accounts that are compromised.
While this helps protect the account data, it is incredibly inconvenient to users, especially in the business environment.
ML, on the other hand, ‘continually learns’ by constantly analyzing new data and looking for anomalies in the system.
When it discovers unusual data that identifies as a threat, the system immediately prompts for additional authentication.
ML, combined with AI, there is a way for companies to implement adaptive controls to safeguard business data while still keeping UX simple.
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