Enterprise security – brought to you by the letters A and I

, Enterprise security – brought to you by the letters A and I

When it comes to securing your organisation, how can you go about best addressing risk? Do you even know where your organisation’s risk exists? Don’t worry, most organisations have a hard time understanding this too, and THIS is what keeps C-level leaders up at night. Here Jackie Brinkerhoff, Senior Director of Product Marketing, SailPoint,delves deeper into the matter.

If you’re a byproduct of the 70s and 80s, you may recall the days of TV shows that were designed to educate as well as entertain. Sesame Street has been one of these long-standing shows. And while we were taught basic life lessons such as being kind to one another, counting, and even understanding emotions, there is one big lesson that we can all bring forward to the present to address risk in the biggest of enterprises. The lesson was a song. Perhaps you might recall ‘One of These Things Is Not Like the Other?’ Jog your memory? Yep, you’re probably singing it in your head now – aren’t you?! Don’t worry; I am too.

When the topic of risk comes up in C-suite discussions, the questions are typical, “What is our risk exposure” and “What are we doing about it?” Answering these questions can be challenging, especially when you don’t know what you don’t know.

For most enterprise organisations, the use of identity governance helps to minimise risk by controlling access for all users to all apps and all data, no matter where it resides across the infrastructure. Limit access – limit your exposure. But again, how to answer the question, “What is our risk exposure?” When it comes to securing your enterprise, adopting a ‘one of these things is not like the other’ is an approach that can uncover risk that you didn’t even know you had. However, this isn’t something that can be done by human power alone – there’s too much identity data also to consider such an effort. So, what if there was a way to quickly see where your risk exists and even better – what if you could see this in real-time?

The good news is that you can now employ a predictive approach to identity that utilises the power of AI and machine learning – we call this SailPoint Predictive Identity. Now you can have your identity platform do the work of finding risk by performing peer group analysis, which flags identities and access that are deemed suspicious (otherwise known as ‘outliers’) due to differences when compared to similar identities.

An AI-enabled identity platform is becoming a game-changer for enterprise organisations since it collects every single event that relates to access across your organisation. Because many enterprises govern access for thousands to millions, to billions of identities, this creates a huge data lake of identity data that is rich with insights that the system then correlates and serves back to you with visibility as to where risky users and behaviours reside. I like to think of it as showing you the needles in the haystack that you didn’t even know you had.

But it goes well beyond that as well, since it uses machine learning, it is continuously monitoring and learning your organisation’s access behaviours and is always adapting and providing recommendations regarding whether role models should evolve, whether it’s safe to grant access to a user, and helping IT staff know what can and cannot be safely automated.

Bringing it back to our little Sesame Street analogy, one way to think of SailPoint Predictive Identity is as an AI-enabled way to help you to decipher between the risks that are clear and present – or nearest in terms of priority. AND helping you to determine if those far-out ‘outliers’ are an area of risky user behaviour or access or something not to be concerned with. For those of you not sure what I mean relative to Sesame Street, this video should help – one of my favourites of Grover showing viewers the difference between the word ‘near’ and ‘far.’ One of those basic lessons learned that even those at the C-level could stand a refresher on, yes?