AI role cyber security strategies for 2023.
Global digitization is accelerating to the point where cyber threats are outpacing human scale and capability.
The enterprise attack surface is massive and constantly expanding in direct proportion to cloud computing, sophisticated networking, system portability, online transactions, coordinated networks, and interconnected servers.
As a result of this unprecedented rate, cybersecurity tools enhanced with Artificial Intelligence (AI) have emerged. But, can AI in cybersecurity solutions truly help mitigate breaches and strengthen your cybersecurity defences?
We know that cyber attackers use AI to carry out distributed denial of service (DDoS) attacks.
These incidents have taught network security officials that AI in cybersecurity and digital ecosystems will help identify vulnerable points, especially if they deal with big data and develop appropriate strategies.
AI in cybersecurity
AI collects data and applies logic to discover connections between risks such as infected files, unusual IP (Internet Protocol) addresses, and cyber threats posed by individuals within the organisation, former employees, partners, and so on.
Because these AI-gathered, logical, and data-driven insights are real-time or near real-time, cybersecurity specialists can respond to cyberattacks and vulnerabilities up to 60 times faster.
Furthermore, machine learning in cybersecurity has increased its value. AI and machine learning (ML) have become a preferred combination for scrutinizing information and network security threats, making it easier to analyse big data and malware trends.
Examples of ML and AI in cybersecurity include: cybersecurity experts can detect and study malware ranging from zero-day vulnerabilities, risk behaviour, phishing, and malicious code downloads.
The combined efforts of AI and ML aid in the proactive reporting of any deviation from previously reported hacker mannerisms or established hacking norms.
Furthermore, this ensures that cybersecurity professionals constantly update their strategies to protect against other risks.
Why do businesses prefer artificial intelligence in cybersecurity?
-Details Rule-based detection systems to handle any false positives during attacks
-Big data analysis Big data analysis combined with constant security update analysis
-IT security monitoring IT security monitoring
-Threat management using artificial intelligence Efficient threat management
-Computer Retrieving affected systems as well as root cause analysis
-Recovering affected systems Threat prediction and trend analysis
-Magnifying-glass In-depth investigation and analysis of threat incidents
What makes the use of artificial intelligence in cybersecurity unique?
One significant advantage of using AI in cybersecurity is that it constantly evolves and self-updates based on ongoing data and information analysis.
Furthermore, these AI-infused systems are iterative, dynamic, and become smarter with each quantum of data analysed.
The management of an organization’s cybersecurity goes beyond human capability.
Effective management necessitates the use of advanced technologies to cover large attack surfaces and keep track of thousands of connected devices, systems, servers, and networks.
As a result, the application of AI in cybersecurity is numerous.
These AI-powered cybersecurity tools gather data from enterprise information systems on their own and send alerts about potential cyber vulnerabilities.
The collected data is analysed and used to perform correlation tests for pattern analysis across millions of signals relevant to the enterprise attack surface.
Any cybersecurity strategy based on data-driven analytics is expected to be much more efficient and cost-effective.
What is the distinction between artificial intelligence and data analytics?
As previously stated, AI is iterative or self-learning and evolves alongside analysis and data reading. Data analytics is a static process that involves examining large data sets and presenting the results using specialised systems and software. It is not iterative or self-learning.
As a result, incorporating artificial intelligence into cybersecurity solutions offers a more proactive approach as well as a constantly evolving feature.
Major advantages of incorporating AI into cybersecurity
The integration of artificial intelligence and cybersecurity enables human teams to improve their enterprise- and domain-specific knowledge.
This is significant because cyber attackers use different attack formats depending on the industry, company size, operational capacity, and so on.
AI-enabled cybersecurity provides the security team with a data repository and an inventory of all the devices, users, and applications present in the enterprise information system.
This allows for improved inventory categorization and management.
Hackers are interested in economic, social, political, and even religious trends. The most recent cyber threats are COVID-19.
The use of in cybersecurity extends to comprehending these trends. Deep learning, neural networks, and natural language processing are used by AI-based cybersecurity systems to study various hacking patterns in large data sets.
Once the hacking trend is recognized, it aids in understanding the potential threat and its impact, as well as the development of a proactive strategy.
The identification of cybersecurity threats and vulnerabilities assists organisations in strategizing and implementing feasible actions, as well as better allocating human resources.
Organizations use AI-enabled bots and other voice assistants to reduce labour costs on mundane and repetitive tasks, rather than allocating skilled labour to these less strategic responsibilities.
According to research, AI-powered voice assistant adoption in mobile will reach 8 billion by 2024, up from 4.2 billion in 2020.
Every day, a typical enterprise receives 200,000 cyber threats.
This figure is so large that a team of cybersecurity experts would never be able to separate the complexity of each of these threats and handle this volume efficiently.
Artificial intelligence in cybersecurity allows for faster prioritisation and response to security alerts, which means that out of 100,000 security alerts raised, it can predict which ones are the most dangerous and deal with them first.
This results in faster response times, more accurate root-cause analysis, and better insights. According to Accenture, AI can increase enterprise productivity by 40% if properly implemented and integrated with the required skill set.
Artificial intelligence and machine learning can have a significant impact on reducing cyberattack risks, but not all organisations know where to start when implementing these technologies.
AI provides cutting-edge cybersecurity solutions, skills, and expertise to both public and private organizations. We offer comprehensive, customised cybersecurity services that include assessment, governance, planning, management, and administration. Fill out the form now to speak with a cybersecurity expert!
