Moving forward, I would like to see machine learning incorporated in to web application scanning, an area untouched by AI today.”
– Doug Barbin, principal and cybersecurity practice leader, Schellman & Company
Fear can be a great motivator. If you are afraid that a human cannot make a decision fast enough to stop a cyberattack, you might opt for an artificial intelligence (AI), machine learning system. But although fear, uncertainty and doubt — the FUD factor — of not responding quickly enough might motivate you to take this action, that same FUD factor that the action your automated system takes might be wrong is an equally strong motivator not to employ this technology. Welcome to this year’s Catch 22.
In the 1983 sci-fi classic War Games, a computer was employed to replace the soldiers who manned the intercontinental ballistic missile silos because, it was believed, the computer could launch the missiles dispassionately and not be swayed by indecision in case of a nuclear attack. A teenager hacked the system thinking it was an unreleased video game. Even someone who hasn’t seen the film can imagine the plot — the machine starts running World War III scenarios and prepares a multitude of real counter-assaults, driving the military IT experts crazy.
Those are the same fears with machine learning today. Just as in War Games, IT can enable today’s security software to not only determine if a cyberattack is occurring, but can empower a server to decide on its own to try and halt the attack, often by logging the suspected attacker off of the network or taking more aggressive actions.
The fear among “let the software do its job” opponents is that only humans should decide on an action, with the risks of autonomous software being too great. These are the experts who argued the soldiers should stay in the silos to turn the launch keys. After all, an “attack” might be false.
Read the full article at SCMagazine