Data science essential for Cyber Security

The topic of data science with respect to cybersecurity. With the correct data, CISO can translate technical risk into commercial risk, present a business case to solve it and prove a success. The current struggle is that CISOs have information that is meaningful, but not timely, or timely, but not meaningful, because the content is very technical and is in silos. What they really need is information that allows them to market and measure the security program. Mike and his team create and apply advanced techniques in data science, computing, and analytics to provide high-value, actionable information to security information managers and security control managers in large enterprises.

Ultimately, data science is allowing the cybersecurity industry to shift from guessing to fact. Over the past decade, the cybersecurity industry has been driven by FUD concerns: fear, uncertainty and doubt. Spending on cybersecurity was justified by the logic that "if we do not have an XYZ widget, you only have to blame yourself when bad things happen." And the bad things are only increasing. The relationship between industry and cybercriminals is asymmetric - attacks are successful due to the challenge that companies face to maintain perfect cyber hygiene - that they have tens of thousands of computers and have tens of thousands of employees using these machines. And just as in the field of counter-terrorism, the adversary only has to succeed once, while the defenders need to hit each time.

This is made even more complicated by the myriad of IT systems and security technologies that have been implemented over the years to protect the company. Often, they do not talk to each other and security officials understandably find it difficult to see a united picture of what is happening.

However, this was to spend blind and justifications in FUD is getting old. Chief Security Officers do not want to operate on instinct - they want and need to be able to develop a value proposition that describes how they are prioritizing what to focus on, justifying and showing how the investment is solving that in ways I can understand. To do this, they must have access to the correct data.

That's where the science of data comes in. With the right data, CISOs can transform technical risk into business risk, provide a business case to resolve it and demonstrate success. The current struggle is that CISOs have information that is significant, but not timely, or timely, but not significant because the content is very technical and isolated. What they really need is data that allows them to market and measure the security program - these are the main gaps in cybersecurity skills that must be closed.

To effectively market the security program, CISO wants to be able to demonstrate risk status and priorities, be able to articulate opportunities, show success and describe to the board where they will get the best performance on a roadmap. The main areas of cybersecurity are an identification (or prevention), detection, response and recovery. There is already much expenditure and an investment in data science approaches in the detection and response space, but in the end, no organization is currently safer as a result.

This is because the root cause is usually not avoided, which requires an improvement in corporate cybersecurity. Obviously knowing that you have been raped is important, but ultimately prevention is better than a cure. This is where new approaches to data science come in. Many large organizations already have a team of data scientists; however, they generally do not work safely. They inform the Chief Data Officer and deal exclusively with business results. For companies that are starting to embrace data science as part of their security strategy, it usually comes from outside consultants.

By working with security staff, data science can be integrated with controls to give a better idea of what to focus on and help manage them by combining technical data to "measure something important" and ensure that data is robust and robust. It is not misleading (accidentally or not).

There are great opportunities at the intersection of data science, Big Data technology and cybersecurity to lay the foundation for companies to gain control over the cyber as a commercial risk. Global banks are at the forefront of hiring data scientists for the security team and adding data to Hadoop environments.

As organizations begin to seek continued visibility into risk performance and security to manage them, there are three fundamental questions that must be answered to determine their ability to take a data-driven approach.

 What is the information we have available and its quality?

What does this mean for the information we can get?

What is our game plan to add and improve our data sources to answer the most important questions?

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