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2020/6/22 16:11:00   中国安防行业网      关键字:国外新品 人工智能 机器学习      浏览量:
  AI and machine learning (ML)
  AI and machine learning (ML) are industry buzzwords that have been used extensively over the past five years or so. Like so many examples of IT jargon they have tended to add more confusion than anything, particularly for the C-suite, as to how it is being applied effectively in the context of information security. However, over the past two years there appears to have been a growing appetite for companies in adopting these innovative tech, says a cyber firm.
  A Capgemini report from 2019 threw some light on this emerging trend, including the fact that 61 percent of enterprises say that they cannot detect breach attempts today without the use of AI technologies, and that 48 percent of companies planned to increase their AI in cybersecurity by an average of 29 percent in 2020.
  It is understandable that companies are looking for new innovative ways to combat the cyber threat. Cybercriminals are using increasingly sophisticated tools and methods to gain access to the valuable data that so many companies now hold. Those companies who are not proactive and sit behind legacy systems in the hope that they are protected, are increasingly finding that they are not.
  The cost of data breaches are also increasing, with the global average cost according to IBM around $3.92m. Add to this cost of regulatory fines (or potential fines), reputational damage and more recently customers suing companies who have experienced data breaches (easyJet is facing a $22billion class action lawsuit over its 2020 data breach). Therefore, the need to find ways to address the threat is crucial and companies are turning to AI. The Capgemini report also saw 69 per cent of enterprises believe that AI will be necessary to respond to cyberattacks.
  One of the stats from the Capgemini saw that 51 percent of executives are making extensive AI provisions for cyber threat detection, outpacing prediction and response by a wide margin. So, whilst many firms see the potential in AI and machine learning in helping to detect threats, most are not using it to its full potential.
  Darren Craig, founder at RiskXchange says: ML models if used to their full extent can determine if a company is at risk of a breach based on an analysis of the company抯 external facing digital footprint. Companies can therefore, be proactive in their defence, rather than passively waiting for AI to identify a possible threat.
  揥ith a sufficient amount of quality data available, ML techniques can easily outperform traditional, manual static based security controls based assessment. Adding risk scoring analytics to a prevention strategy built on AI/ML learning is a great way to raise the bar in securing today抯 corporate networks.
  揑t is promising that companies seem to be starting to embrace AI/ML for cybersecurity, but in order to be more fully secure, companies need to take a proactive approach, using the technology抯 full potential. In doing so companies are in a much stronger position to protect themselves from an increasingly sophisticated criminal, and the huge associated cost.

  RiskXchange创始人Darren Craig 表示:“如果充分利用ML,则可以根据对公司外部数字足迹的分析来确定公司是否有遭受违约的风险。因此,公司可以进行主动防御,而不是被动地等待AI识别可能的威胁。
  “有了足够可用的高质量数据,机器学习技术就可以轻松胜过传统的基于手动静态安全控制的评估。将风险评分分析添加到基于AI / ML学习的预防策略中,这是提高保护当今企业网络安全性的绝佳方法。
  “值得一提的是,企业似乎已开始采用AI / ML来保护网络安全,但是要想更加充分地保护其安全,企业就需要采取更加积极主动的方法,并充分利用该技术的潜力。这样一来,企业就可以保护自己免受日益复杂的犯罪分子和相关巨额费用的侵害。”