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Some of the world’s best-known brands have invested millions of dollars in information security. So have their adversaries. But malicious actors are counting on the fact that your defenses are operated mostly by humans and tend to be the same across the board.
When you moved into your neighborhood, did you change your locks or do you have the exact same ones as all your neighbors? Think about what could happen if a thief compromised just one of those shared locks? For some reason, the world of information security has a same-lock mentality. And some “customers” are malicious actors working hard to do harm to the rest. Given this situation, we should not be surprised that even with the massive amount of money being spent, defenses still fail.
If cyber defenders are ever going to have a chance at winning, we must begin to level this playing field. Vendors distribute identical copies of their security products to customers because it’s easier for them, not because it’s better for their customers.
How many variants of a signature is an anti-virus company supposed to produce for each malware sample it analyzes? Do all host-based artificial intelligence (AI) defenses learn in their environment? In the past, tailoring these approaches for each enterprise was not feasible. Luckily, new techniques are emerging within cybersecurity that produce unique detection behaviors for each customer, behaviors that can help level the playing field and maybe even help win the game.
These emerging techniques broadly fall into the area of AI and machine learning. At the heart of any AI system is the ability to learn. Some AI solutions learn from their local environment, while others learn strictly from a global context. Those that will win out are solutions that build some or all of their threat detection capability using…