GA Systems artifical intelligence cybersecurity

Artificial Intelligence in Cybersecurity

The term “cybersecurity” refers to the systems that organisations put in place to counter malicious online attacks and safeguard networks against unauthorised access.

In 2019 you’d be hard-pressed to find a business or organisation that doesn’t rely on computer networks to process, move or store private and potentially sensitive information. As you might imagine, the need for security in the digital space is paramount and all-encompassing – after all, it’s one thing for a hacker to steal information about your gym membership, but extend that to the plethora of private and powerful information passing through banks, governments and military organisations, and the threat of cyber attack becomes starkly clearcut.

Whether this information consists of personal details, company financial data or the latest nuclear launch codes, the exposure caused by a lapse in cybersecurity has the potential to create irreparable damage to individuals and society. By 2021, cybercrime is projected to cost the world $6 trillion a year.

As our reliance on technology grows, the use of artificial intelligence (AI) to combat cyber threats becomes the natural progression of cybersecurity: after all, AI algorithms can be made to track and address thousands of potential threats in the time it would take a human analyst to deal with a single attack, and it doesn’t sleep.

The problem? We aren’t the only ones using AI to our advantage.

Cyber-attackers are building their own AI algorithms to outsmart, undercut and overwhelm friendly systems, necessitating an ongoing investment into the development of AI cybersecurity if businesses and organisations are to keep their data – and that of their users – safe.

The role of AI in cybersecurity

As is the case in more traditional forms of warfare, it’s the aggressors that have first-mover advantage, with much cybersecurity developed in response to cyber-attacks.

Cyber-attackers are investing in building automation into their software, putting significant strain on to defending systems, many of which will require manual intervention to fend off hostiles until a more sustainable automated solution can be worked out. Of course, while the defending organisation is busy using its resources to build a suitable defence against one threat, it will be vulnerable to attacks from others and less able to spot system intrusions, giving attackers the opportunity to exploit vulnerabilities, compromise systems and steal data.

The only solution to this level of self-sustaining virtual attack is AI. There is no way for a human being to manually keep up with the level of threat analysis and mitigation needed to prevent attacks from a malevolent AI, so systems must be developed to monitor infiltrations, combat attacks and alert people when human intervention is required.

How AI helps

With cyber threats becoming increasingly sophisticated, human security teams simply cannot keep up with a digital attacker that doesn’t sleep. The answer? Autonomous detect and response systems. These automated AIs now form the base layer of protection for most cyber threat response systems, using machine learning and real-time countermeasures to recognise risks, log threats and fight off would-be attackers.

But to understand how this process actually takes place in practice, you need to have some industry jargon under your belt, namely the difference between AI, machine learning and deep learning.

What’s the difference between Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning?

Taken at face value, the term “Artificial Intelligence” is pretty much self-explanatory: it’s a program that simulates human-like intelligent behaviour in computers. It does this by using predetermined algorithms and iterative processing to sort through and identify patterns in enormous quantities of data. By spotting these patterns in behaviour, the AI learns to make predictions and “intelligent” decisions.

The advantage of having a machine rather than a human being carry out this analysis is quantity. Where a person might spend an entire lifetime manually parsing through a database of information, a machine is able to devour the same amount of data and give you a list of outcomes in mere minutes.

Both machine learning and deep learning fall under the umbrella of AI.

Machine learning

Machine learning is the term used for the subset of AI that uses mathematical and statistical analysis to identify repeating patterns in databases, and use the resulting information to make informed decisions. While machine learning algorithms are able to do this without being given explicit instructions, some human intervention is still ultimately required to tell whether or not these decisions make sense.

Most examples of AI that you encounter day-to-day – such as driverless cars or computerised chess champions – rely on a combination of machine learning and natural language processing. Netflix’s recommendations are an example of machine learning. By comparing your watch history with that of other users, Netflix’s machine-learning algorithms are able to suggest new shows you might enjoy based on the preferences of people who have expressed similar behaviour to you. Spotify does the same thing.

Deep learning

The next layer of this Russian doll of robotic complexity, and despite the term being used interchangeably with “machine learning”, deep learning is, in fact, a subset of machine learning.

The difference between the two is that deep-learning algorithms are structured to create artificial neural networks similar to the way we believe the human brain works. Using these networks, deep-learning algorithms are able to learn from data, make predictions and determine without any human intervention whether or not their predictions are accurate. The most human-like examples of AI are powered by deep learning.

The differences between AI, machine learning and deep learning might appear pedantic at first glance, but the three operate in distinct ways that can be made to complement one another in pursuit of a cybersecurity utopia. This unification of resources towards an autonomous and intelligent artificial mind is known as cognitive computing.

Cognitive security

Cognitive security is the use of AI technology to learn how human thought processes work and use that data to detect threats to digital and physical systems. It is based on the principle of cognitive computing – an advanced form of manmade intelligence that leverages various forms of AI, including deep learning networks and machine learning algorithms, to become smarter and more powerful with experience.

By operating a model of continuous learning, cognitive security systems are able to interpret information in a way that allows them to spot behavioural inconsistencies in a subject. Such a system can carry out an independent assessment much in the way a human security analyst would – developing its own hypotheses and acting upon them with the advantage of greater capacity and speed than its human counterparts could ever be capable of.

Advantages Of Artificial Intelligence In Cybersecurity

Compound intelligence

If knowledge is power, then AI can easily trump that of any human being.

Think of it this way. If you train your AI on your company’s star employee, you will have effectively cloned that worker’s productivity, at least as far as business deliverables are concerned. What about if you trained your AI on the top ten employees? Or the top 100 employees in the world? Because the learning outcomes stack, your AI will be able to use the compiled experience of all those people to conduct its work.

Expand that analogy to the analysis of, for example, behaviour patterns that lead up to a malicious attack, and the result is a Minority Report-style machine that can predict when and where a security breach will occur, and carry out the measures necessary to prevent it.

Always on

Since AI does not suffer the human shortcomings of hunger, lethargy and ennui, it can continue to consume data and develop its intelligence around the clock, making it the most vigilant guardian any managed security service can hope to have. Better yet, AIs don’t falter in their attention and performance because they feel a bit sleepy or peckish, instead delivering their role at maximum efficiency and remaining fully alert around the clock. In addition, preprogrammed notification modes can be built into the AI to alert stakeholders in record time should a security breach occur.

Real-time response

Legacy security software tends to be restricted in scope, with static databases and isolated programs slowing response times and limiting what the system can do if an attacker acts in a way the system doesn’t expect.

An integrated AI, on the other hand, can handle the entire operation, from observation to reporting and mitigating the threat even if that threat mutates along the way. Dedicated algorithms devoted to keeping a lookout for potential threats mean that enforcement can happen in real-time should an attack occur. Because the AI’s algorithms have the ability to learn from experience, they can keep up with and anticipate threats even if these are slightly different from what they may have encountered before.

Success rate of AI in cyber attack prevention

The success rate of AI use in cyber-attack prevention ranges from 85% to 99%, depending on which research paper you read, with world-leading cyber-threat defence company Darktrace consistently holding the top position by pairing a 99% success rate with a supremely low level of false positives.

Types of AI applications used in cybersecurity

All new code will have vulnerabilities that can be exploited by hackers. As the software develops and is tested, many of these vulnerabilities are accounted for, but this process takes time and resources, leaving an opportunity for malicious coders to attempt to infiltrate the system and manipulate it to their advantage. Of course, new software and updates are being created all the time, so what are the chances of a hacker being interested enough in yours to devote the time it takes to hack into it?

Now imagine that this hacker isn’t a person, but an AI. Suddenly, the infiltration becomes infinitely scalable so there’s no need to pick and choose: just let the malicious AI run and do the dirty work. This is what’s currently happening. We’re seeing computer viruses capable of getting into a system, learning from it and changing the way it behaves.

Preventing these sorts of intelligent attacks requires equally intelligent stalwarts that not only guard your system, but search for infiltrations and repair any errors and carry out vulnerability management.

Here are just a few of the myriad ways AI is being leveraged to improve cybersecurity:

  • Botnet detection
  • Forecasting of hacking incidents
  • Biometric identification
  • Network intrusion detection and prevention
  • CASB solutions
  • Fraud detection
  • Credit scoring
  • Secure user authentication
  • Artificial intelligence cybersecurity systems
  • AI network security algorithms
  • Cyber security ratings