How do we build a genuinely effective early-warning system

Irrespective of the age or context, the ability to have information in advance is deemed to be of great strategic advantage. This has been true for the Greeks to get forewarned on Xerxes’ plans for invasion, for the British tabloids to station their reporters at the right place and for security professionals to be alerted on the motives of new threats. However, it is important to appreciate the difference between data and actionable insight. Analysis on potentially the greatest intelligence failure of the last century is quite instructive.

"It is much easier after the event to sort the relevant from the irrelevant signals... Before the event [a signal] is obscure and pregnant with conflicting meanings... In Washington, Pearl Harbor signals were competing with a vast number of signals from the European theater... In short, we failed to anticipate Pearl Harbor not for want of the relevant materials, but because of a plethora of irrelevant ones." - PEARL HARBOR: Warning and Decision. By Roberta Wohlstetter (SUP-1962).

Why is it such a challenge for most organizations to make sense and take proactive measures around mitigating impending risks? We believe that the issue is not so much having access to all the information - but in connecting the dots.

The following are the reasons why this is a problem for most organizations:

Handle large data volumes: It is critical not to be constrained by working on small data samples or cherry-picking relatively cleaner easier data feeds. These calls for handling data exhaust (logs) and archived transaction records etc. - with all the elements of format variety, varying quality levels and frequency
Speed of analysis: In many cases, the impact of the risk can be mitigated if the detection is fast enough. Over time, the evidentiary value is lost and the damage done cannot be recovered
Collaboration across groups: Data becomes more actionable intelligence when it is correlated from multiple perspectives. Without the integrated data view, the true picture of risk doesn't emerge and it is easy to confuse noise for signals. This becomes a challenge in most enterprises where alerts raised in one function do not get shared with other groups
Creating the right mind-set: Given the challenges above, the traditional ways of handling and analyzing data wouldn't be effective. The good news is that there has been an explosion in techniques and tools that could be used by a risk professional

To reduce complexity of handling the problem, it would help the Risk and Compliance function if they had a platform that could leverage big data technologies and an ability to orchestrate the entire data flow from sourcing to case management and reporting. Apollo has been built with that vision.

Wipro has built the Apollo^TM platform for Fraud Control using Big Data Analytics that has been deployed in various use cases in multiple industry domains. For details on the platform and underlying philosophy, please visit the Apollo^TM webpage.

About the Author

R.Guha heads Corporate Business Development at Wipro Technologies and is focused on two strategic themes at the intersection of technology and business: cybersecurity framed from a business risk perspective and how to leverage machine learning for business transformation.

Guha is currently leading the development and deployment of Apollo, an anomaly detection platform that seeks to mitigate risk and improve process velocity through smarter detection.

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