Headlines about the future of cybersecurity range from dark: “The first step in defending yourself is knowing your enemies,” to optimistic: “…technology naturally evolves to become more secure against the threats against it.”
In this three-part series, we’ll explore some of the trending topics about cybersecurity. Our goals? To educate and inform. Because as Fox Mulder from the X-Files often says, “The truth is out there.”
Our first post examines the myths (and facts) about the impact of artificial intelligence (AI) on cybersecurity technology.
Artificial Intelligence Defined
Confusion exists about AI because it’s broadly applied across our culture. We use the term ‘AI’ when asking our personal assistant, Siri or Google AI to book a table at our favorite restaurant. Radiologists describe the software used to detect cancer as ‘AI-powered.’ AI is not about choosing options from a pre-programmed list of alternatives. When a machine creatively solves a problem based on analysis of existing conditions, it’s using artificial intelligence. AI makes a machine ‘smart.’
You’ll sometimes hear machine learning and AI discussed in the same sentence. Machine learning is a way to achieve AI. Machine learning is the set of rules or the algorithm that when applied to data, can recognize patterns and classify new data based on available information.
These rules help the machine learn. You benefit from machine learning each time you begin a search with ‘artificial’ and auto-fill adds ‘intelligence’ because you’ve searched the topic in the past. In the world of cybersecurity, most of the hype around AI truthfully refers to machine learning. Most security applications can’t extrapolate new conclusions without a large quantity of data.
Use of Machine Learning in Cybersecurity
AI is used in marketing. But will AI have an impact on cybersecurity technology? Our answer is a resounding, “Yes, because it’s already actively involved.” Machine learning currently has a role in cybersecurity helping uncover new viruses and malware. Threat detection capabilities are evolving, but broad examples of AI-based machine learning applications include the following:
- Companies use AI-based solutions to analyze a website or file, determine it isn’t performing within usual guidelines, and identify it as potentially malicious.
- In addition, machine learning can identify software weaknesses and configuration flaws, calling out high-risk situations.
- Machine learning can also use known patterns from ‘big data’ to recognize malware before it’s broadly distributed.
AI-Based Machine Learning is Collaborative but Not Invincible
Few AI-based cybersecurity tools are ‘stand-alone’ solutions. Machine-learning enhances existing security protocols by analyzing and eliminating ‘noise’ or inaccurate alerts. AI-analytics are often paired with other technologies in a company’s security architecture.
The challenge? Staying 10 steps ahead of hackers, who are using machine learning to introduce misleading data to skew algorithms. Security firm Cyxtera used publicly available data about historical phishing attacks to create an AI-based attacker that bypassed detection in 15% of attempts. Reducing the blind spots in machine learning remains an ongoing quest for companies like Cyxtera and cybersecurity researchers.
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Up next in our Future of Cybersecurity series: How Crypto is Used to Enhance Cybersecurity.