$3.86 million and more than six months’ worth of work – that’s the cost of a standard data breach, shows a report by Norton. Add to it the damage to reputation, business risk, and innumerable hours that cyber security professionals have to put in to repair the damage. Cyber-attacks are the biggest threats to any business today. It is estimated that damage due to cyberattacks will cross $10 trillion by 2025, a 300% increase from 2015. With such a relentless onslaught that is getting more sophisticated by the day, the role of artificial intelligence (AI) and machine learning (ML) becomes integral to identifying, preventing, and neutralising these threats.
In the age of work-from-anywhere and always-online mode, companies are so much more exposed to cyber-attacks. Experts found that a midsized firm gets more than 200,000 alerts of cyber events every day. It is not humanly possible to handle this by smaller IT teams. This article delves into why AI and ML are turning out to be the solution that most organisations are turning to so that they can fortify themselves from cyber-attacks.
How AI/ML gives you the edge
The latest Ponemon Institute Cost of a Data Breach Report highlights that automation can halve the cost of a data breach and contain threats in about 77 days. Handling large amounts of data, and analysing it within minutes aid cybersecurity professionals in taking decisions within a short period of an incident. Here are five features that keep AI/ML ahead of traditional systems:
- Learns on the go
The technology is built to learn from datasets and improve network security. As the record of cyber-attacks build up, it recognises patterns, detects anomalies, and takes action more accurately. Over a period, it can predict and blocks similar types of attacks.
- Tracks Unknown threats
Hackers launch innumerable attacks using new methods that include malware attacks and sophisticated social engineering. AI, powered by ML, has proven to be one of the best technologies in tracking and preventing unknown threats.
- Scans for bots
According to research by Imperva, bots constitute more than 40% of global internet traffic. The majority of cyber-attacks such as account takeovers and fake profiles are done through bots. AI systems can be trained to differentiate between malicious and good bots.
- Equipped to handle different problems
A phishing attack, ransomware, or identity impersonation, all of these can happen at the same time. It is important to prioritise the action plan to handle them, here AI can help detect the different types of attacks and analyse the degree and type of damage to help you decide which one should be handled first.
- Strengthens security
Overall, the technology helps identify weak links in the network security system and helps manage vulnerabilities and secure systems against future attacks.
Benefits of AI and machine learning
Nearly 95% of cybersecurity breaches happen due to human slip-ups such as setting a weak password or downloading a corrupt email, shows a Google survey. In such a situation the use of advanced technology becomes paramount. A recent survey reported that 61% of organisations said that without AI they would not be able to detect critical threats.
Quicker response time – Decoding and tracing
data in seconds and implementing patches to remediate threats in almost real time
ensures apprehension without much damage.
Reduced IT costs – According to a report, the average cost reduction is 12% due to the effective use of AI/ML.
More efficient IT team – The Workload of the IT team is reduced as they no longer have to manually wade through data spanning years to come up with an effective solution. Rather the technology augments the support that the professionals require to prepare action plans.
Areas of application
- Detecting frauds
In 2021, Javelin Strategy & Research suffered $28 billion in losses due to identity fraud, which left 27 million U.S. consumers vulnerable. Financial institutions can identify and prevent such breaches with AI that creates a database of transaction patterns and can flag any discrepancies proactively.
- Reduce false positives
When IT teams analyse millions of online cyber threats, there is a chance of false positives. Implementing smarter cybersecurity technology can help reduce the number of alerts as ML can distinguish a threat from others.
- Minimise human error
A IBM Cyber Security Intelligence Index report shows that human error was a major contributing cause of cybersecurity breaches. Systems that are automated are the first line of defense, reducing the incidence of such errors.
- Checking advanced malware
A ransomware attack was recorded every 11 seconds in 2021, up from 40 seconds in 2016. AI cybersecurity threat detection systems are particularly useful for tracking polymorphic and metamorphic malware that can keep changing a portion of their code to avoid getting detected.
In today’s hyper-connected world, cyber defense strategies need to encompass vendors and customers, apart from employees. Always looking to punch harder, cybercriminals keep modifying their malware code so that security software does not identify it as bad. Amid such cyberwarfare, advanced tools such as AI/ML are increasingly becoming essential as the arsenal of cybersecurity teams of organisations. While these technologies are also evolving, they are still emerging to be an effective way to neutralise threats.
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