How AI is the Future of Cybersecurity ? What AI and Machine Learning will bring for CyberSecurity like wise we have many questions yet to be answered
This is the first post in “AI role in CyberSecurity” series by #AILabPage, second post is available here
AI in CyberSecurity
In a cybersecurity context, AI is a software that perceives its environment well enough to identify events and take action against predefined purpose. It can also learn and build the rules on the go as well; actually thats the real AI. Biggest fear of today’s time is the concern that hackers are getting much more smarter. These hackers will use artificial intelligence in cyberattacks that are more sophisticated and harder to detect.
We do have Artificial Intelligence in our systems and business strategies. Still there is a frightening truth about increasingly common cyber-attacks. Which is most businesses and the cybersecurity players itself are not prepared for better use of AI.
AI has already proven to be both a benefit and a threat on the cybersecurity front. Barclays Africa is beginning to use AI and machine learning to both detect cybersecurity threats and respond to them.
Artificial Intelligence depends upon Natural Intelligence Gaps
While the technology can help to fill cybersecurity skill gaps but at the same time its a powerful tool for hackers as well. In short AI can act as guard and threat at same time. What matter is who use it for what purpose. At end It all depends upon Natural Intelligence to make good or bad use of Artificial Intelligence.
There are paid and free tools available which can attempt to modify malwares to bypass machine learning antivirus software. Question is how to detect and stop?
Cyberattacks like phishing and ransomeware are said to be much more effective when they are powered by AI.
87% of US cybersecurity professionals report that either they are currently using or will use AI as part of their cybersecurity strategy.
On the other hand to power up the behavioural patterns AI in particular is extremely good at recognizing patterns and anomalies. This makes it an excellent tool for threat hunting.
Will AI be the bright future of security as the sheer volume of threats is becoming very difficult to track by humans alone. May be AI might come out as the most dark era, all depends upon Natural Intelligence.
Natural Intelligence is needed to develop AI/machine learning tools. Despite popular belief, these technologies cannot replace humans (in my personal opinion). Using them requires human training and oversight. AI is here to stay and it will have a large impact on security strategies moving forward. As results reveals Natural intelligence needs improvement as well.
Artificial intelligence Just Made Guessing Your Password a Whole Lot Easier
What can AI do for cybersecurity as a guard and as a terminator?
Cybersecurity state as on date is too much vulnerable but implementation of AI systems into the mix can serve as a real turning point. These systems come with a number of substantial benefits. These benefits will help prepare cybersecurity professionals for taking on cyber-attacks and safeguarding the enterprise.
Deploy AI and machine learning-based tech to help. With these helps tasks like policy enforcement, blocking malicious files and IPs, and protecting against phishing attacks. Machine learning won’t replace human intelligence rather should not be aimed for as well. These technologies can be use to automate and speed up security operations and repetitive tasks, according to the release.
91% of cybersecurity professionals are concerned about hackers using AI in cyberattacks.
Data Mining for Intrusion prevention and action in real time can be done to avoid misuse detection. Predictive models should be built from labeled data sets i.e for instance labeling data as “normal” or “intrusive”. Action required on top of these rules.
These models can deliver more sophisticated and precise solutions than manually created signatures based rules. Then challenge our self where we are unable to detect attacks whose instances have not yet been observed should become part of machine learning and on the fly building the same.
Anomaly detection to build models of “normal” behaviour and detect anomalies as deviations from it. Possible high false alarm rate – previously unseen but may be legitimate can support system behaviours to be recognized as anomalies.
Integrating Artificial Intelligence into Cybersecurity can boost transparency levels in this playing field.
Security should be treated as a fabric for your network and business, which takes a platform approach. It can take threat intelligence gathered through Big Data analytics. After which it can automatically alert all inter-connected devices and with updated prevention.
AI based signatures can work lot better. All this is at a speed, low cost and should be within the reach of any organisation entrusted with. How it will be achieved depends upon next step; which is the discussion point at length in a workshop.
In general it is said firewalls no longer stop hackers but AI based cybersecurity can stop hackers though. The cybersecurity industry has always had a fortress mentality: Firewall the perimeter! Harden the system! This mindset has failed, miserably. Each new headline-generating hack has reminds us. Even if you do patching on all software’s you could still be at risk. Its been proven already with attacks on cybersecurity.
Past & Future of Threats & Protection – Year 2017 was dominated by news of major hacks, cybersecurity threats and data breaches. What will 2018 have in store?
Year 2017 was dominated by news of major hacks, cybersecurity threats and data breaches. What will 2018 have in store? We will see it in many ways why AI is crucial to cyber security. Threat detection is certainly a main focus of today’s AI together with machine learning technology push. Not only it can monitor human behavior, but also it can detect things that aren’t quite right and sound an alert.
As Enterprises are increasingly being entrusted with private data, the implications of leaks are potentially huge and wide-ranging. It is not just the individuals but big corporates in large numbers worldwide that are at risk. With correct guidance, direction and strategy business can lead to over come. AI machine learning has established what familiar patterns look like and can recognize normal traffic.
Machine learning is both cool and valuable, but to apply it effectively requires that we disregard the former in order to be rigorous about the latter. In this session we take a hard look at the qualities that make machine learning fit for purpose for problems in cyber security in order to lay out a global roadmap of how machine learning can help.
Some Questions to be answered – How are Machine Learning and Artificial Intelligence Shaping & securing the future of cybersecurity? What is required here to make this work is a jump to lowers Cybersecurity costs.
Perhaps the most relevant answer is employing Artificial Intelligence. I have not used deploy AI as employ is more relevant here for this work in a reward model.
If we don’t harness the power of artificial intelligence into cybersecurity then, hackers will eventually do this. Its been known and said by many scientists who have got success in harnessing the power of artificial intelligence to create a program that, combined with existing tools, figured more than a quarter of the passwords from a set of more than 43 million profiles (One of the biggest social media platform).
In info-security industry that comes first with leadership roles with best-developed products and excellent professional services, this will be known as the winner. Yet the researchers say the technology may also be used to beat baddies at their own game.
Conclusion – Revenue leakage due to Cybersecurity are the biggest revenue source for Underground Economy.The future of IoT spells blockchain and AI – the reality of blockchain today mainly spells cryptocurrency and hype. As on date we have 3 major challenges to our privacy and those are technology evolution, burgeoning security establishments (Face recognition) and no stop higher revenue motivations. The importance of a contextual view of threats that also incorporates visibility and data points from networks, endpoints, and human threat researchers to derive the most accurate cyber risk assessment.
Disclaimer – all credits if any remains on the original contributor only. Number stats taken from Webroot, 2017
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