liu, tempo Date: 2021-09-16 10:38:06 From:cengn
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After an explosion in the early 1860s killed his younger brother, Alfred Nobel invented dynamite, allowing miners and prospectors to dig through stone faster and safer.


However, Alfred Nobel was saddened and regretted his invention when he soon discovered that people used dynamite to harm others. Worried about his legacy, Alfred Nobel created the Nobel Prizes to reward those who have conferred the greatest benefit to humankind.


Similar to dynamite, artificial intelligence (AI) is only a tool, and it’s up to us to choose how we use it.

Throughout this article, we’ll highlight how AI is used to protect organizations. But we’ll also show you how AI is used to hack and steal from organizations.

Before we begin, let’s start by addressing the growth of artificial intelligence in cybersecurity today.



AI is a well-known asset for businesses that use automation to increase productivity and streamline tasks. One crucial application that leverages AI more than any other today is data security, or cybersecurity . With the economy going through a digital transformation and data breaches increasing by the day, AI might be our best chance at safeguarding our information.


The global market for artificial intelligence in cybersecurity is projected to reach $46.3 billion by 2027, at a compound annual growth rate (CAGR) of 23.6% during the forecast period . This stems from the increasing complexity and frequency of cyber threats, leading to a need for disruptive cybersecurity solutions.


This explosive growth results from increasing government investment in the security sector and the requirement for cloud-based security solutions among businesses regardless of size or industry. As the network capacity grows with 5G technology and improved cloud services, so does the growth of artificial intelligence and the arms race between cyberattacks and cybersecurity.



As technology progresses, more of our information has been moving to the digital world. As a result, cyberattacks are increasingly common and costly. Business organizations, for example, are desirable targets to cybercriminals simply due to the large amount of data that hackers can steal in a single attack.


Globally, a company’s average total cost per data breach was $3.86 million in 2020 . The Ponemon Institute study identified that organizations with fully deployed security automation saved on average $3.58 million compared to those with no security automation in place.


When used in conjunction with traditional methods, AI is a powerful tool for protecting against cyber attacks. In the digital age, shielding assets and operations from hackers is more challenging than ever. The numbers are staggering – Cisco alone reported that they blocked seven trillion threats on behalf of their customers in 2018 .


At the same time, the adoption of artificial intelligence is not without risks: more than 60% of companies adopting AI recognize cybersecurity risks generated by AI-powered threats as the most relevant ones to business operations . Although AI provides organizations with the tools to fend off malicious attacks in the wrong hands, it can supply cybercriminals with avenues to take advantage of systems.


The following sections will go over the main functions of AI in cybersecurity and the benefits and drawbacks the technology offers to protect data.





In the domain of cybersecurity, AI has three primary functions:



and Response



Today, organizations use AI extensively to detect cyber threats. Over 50% of organizations implementing AI-based cybersecurity solutions have a high usage rate for detection purposes. Detection reflects the unique capabilities of AI, through machine learning or deep learning, to use behavioural analysis to identify irregular traffic continuously.


For example, Honeywell recently launched their Secure Media Exchange (SMX) solution, which leverages AI to detect threats introduced via USB devices to disrupt operations by misusing legitimate USB functions or unauthorized device actions..



The prediction function holds the second-highest usage rate. Roughly 35% of organizations make extensive use of AI to predict cyber threats. By scanning through vast data types, the AI makes predictions based on the system’s training.


Organizations that employ AI for prediction purposes can use the technology to automatically identify their assets and network topology, identify critical vulnerabilities, and continuously improve their networks defences against any potential cyberattacks with destructive potential .



Lastly, when it comes to responding to threats, AI is still evolving. Only 18% of organizations make extensive use of AI to respond to cyber-attacks. This means automating the creation of a virtual patch for a detected threat or developing new protection mechanisms in real-time.


An AI response solution is straightforward, detecting attacks and stopping them while they are happening. U.S. specialty retailer Avenue deployed a machine learning-based solution to differentiate between normal and abnormal behaviour to combat bot attacks, shutting down bot-triggered anomalous behaviour like stolen credentials or unauthorized purchases on customer accounts .


Regardless of how an organization uses its AI resources for cybersecurity, it helps them improve threat response time, lowers costs, and responds to breaches.



Among the main benefits that AI brings to cybersecurity, we can point out the following:



Successful threat hunting requires a pre-emptive search of large data sets, using AI and machine learning to power automated scanning. This approach to AI is proactive rather than reactive. The idea is to identify threats continuously that may or may not have already evaded the current detection capabilities.


However, it’s crucial to incorporate the AI approach with traditional security techniques, also known as signature-based systems. These include firewalls and malware detection programs. Signature-based methods are efficient in battling previously encountered threats. Still, AI methods are more effective to identify threats that have not been discovered yet, going through large volumes of data, and integrating behavioural analysis.


Automating threat hunting with AI enables faster response times and improved recommendations on responses. It provides the information needed to reduce attack vectors, breaches and allows organizations to move from a purely reactive response to operating ahead of threats, predicting new attacks based on past occurrences and data.



Unlike threat hunting, which focuses on identifying and neutralizing incoming malicious agents to the system, vulnerability management identifies weak spots within the organization’s network environment and assets.


With AI tools, organizations can analyze the baseline behaviour of user accounts, endpoints, and servers, identifying abnormal behaviour that might signal an unknown attack. This helps organizations protect themselves from potential threats targeting system weaknesses. Vulnerability management includes identifying vulnerabilities in IT assets, evaluating risk, and taking appropriate action across systems or networks.


Thanks to machine learning and deep learning techniques, cybersecurity systems can learn and evolve from each success and failure it experiences, optimizing performance for future encounters in real-time to assure the network’s integrity.



Many attacks succeed because organizations don’t set well-timed policies or apply firmware updates or patches to all devices throughout the network. As new technologies constantly flood the market, providing a competitive advantage, organizations are pressured to incorporate them into their network, compromising security quickly.


Organizations can leverage AI to improve network security by learning network traffic patterns and recommending functional groupings of workloads and security policies based on specific needs. Additionally, AI can help manage the vast number of connected devices navigating their firmware updates and security patches automatically – something that would take considerably more time and potentially increase risk if done manually.


Additionally, AI for network security can be combined with other innovative technologies such as blockchain. Blockchain enables secure storage, data sharing and helps security management identify criminal identity loopholes in the system. Combining AI and blockchain, organizations can guarantee a higher security standard for their data and avoid compromising the whole system against an attack.

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