The world is developing at an unprecedentedly fast pace, and the changes are taking place even faster. The word digitalization implies everything is moving at lightning speed, including trends, businesses, and new products. Consumer interests are served almost instantly with various means available to the service providers to deliver them.
With digital evolution in all spheres of life, the threat to cybersecurity increases with each passing day. These days cybercriminals are making use of cutting-edge technologies to create cyber attacks on large organizations or corporations.
Earlier, traditional tools such as anti-virus software, firewalls, web proxies, etc, were used by organizations for security. However, with digitalization, there has been a dynamic shift in the cyber threat landscape, reducing these traditional security network tools’ effectiveness.
Many companies these days are using Artificial Intelligence (AI) and Machine Learning (ML) tools to prevent the growing number of attacks on networks. With the world moving towards technologies like the Internet of things and connected devices, AI’s deployment is crucial for cybersecurity firms to analyze massive amounts of data to save networks from infiltration efforts.
How Artificial Intelligence (AI) improves Digital Security?
AI ideally solves some of the most challenging problems, including cybersecurity problems. With the increasing proliferation of devices and cyber attacks, Machine learning and Artificial Intelligence can help automate threat detection and respond efficiently compared to traditional cybersecurity tools.
An AI-based self-learning cybersecurity management system can solve many challenges that pose a threat to network security.
Many technologies are available to train a self-learning system to independently and continuously gather data from enterprise information systems. This gathered data is analyzed and used to perform the correlation of patterns across various signals relevant to the enterprise attack surface.
AI provides a new level of intelligence in diverse categories of cybersecurity, including:
- Threat Exposure- Hackers keep changing their approaches regularly with changing trends. AI security systems provide up-to-date knowledge on industry-specific and global threats and prioritize the decisions based on the possible threats that your organization might face.
- Incident Response- AI-powered systems can provide response and prioritization to security alerts with improved context enabling a faster response to incidents and surfacing root causes to avoid further issues in the future.
- Controls Effectiveness- AI helps you understand your program’s strengths and gaps and provides you with a proper understanding of various security processes and tools and their impacts on maintaining strong security.
- IT Asset Inventory- Artificial Intelligence (AI) helps gain an accurate and complete inventory of all users, devices, and applications with access to any information systems. Categorization of business play a crucial role in inventory.
- Breach Risk Prediction- With the help of AI-based systems, organizations can predict how and where they are most likely to be breached to plan tools and resources towards areas of weakness properly. Insights derived from AI help you enhance and configure processes and controls to improve your organization’s cyber resilience.
The effective integration of AI- technology into your existing cybersecurity systems helps you create more accurate login techniques based on bio-metrics, detect threats and malicious activities using detective analytics, secure conditional access, and authentication, and enhance learning and analysis with natural language processing.
Once AI is integrated into cybersecurity solutions, IT security employees and cybersecurity analysts should know how to use it effectively. The use of AI can be a huge boon to protect your organizations digital security. Many leading companies like Google, Fireye, Crowdstrike, IBM, and Juniper networks have already integrated AI security tools into their cybersecurity systems.
However, it is not sufficient to introduce AI tools for a cybersecurity purpose. It is equally essential for organizations to continually monitor and understand loopholes in the security systems as hackers are becoming sophisticated daily. For this purpose, few organizations are hiring ethical hackers and are turning towards crowd-sourced security measures to identify loopholes in their security systems.
Limitations of AI in cybersecurity
No matter how advanced and efficient AI technology is, there are also few drawbacks and limitations that prevent Artificial Intelligence from becoming a mainstream security tool:
- Resources – To build and maintain AI systems, organizations need to invest a lot of money and time on resources like data, memory, and computing power.
- Hackers also use AI – Hackers learn more advanced attacks from existing AI tools to develop attacks on traditional security systems, including AI boosted systems. Attackers keep testing and improve their malware to make them resistant to AI security tools.
- Data sets – security Teams need to understand many data sets of malware codes, malicious codes, and anomalies as AI models are trained with learning data sets. Few companies lack the time and resources to obtain all accurate data sets.
- Neural fuzzing – It is a process of testing a large amount of random input data within the software to identify vulnerabilities. AI can test a large amount of random data with neural fuzzing. However, hackers can gather information with neural networks to understand the weakness of the target system.
Artificial Intelligence in cybersecurity comes up with a risk in the form of adversarial AI, which refers to the development of AI for malicious purposes. As AI increasingly moves into a company’s cybersecurity practices, companies need to keep an eye and guard against AI technology’s potential downside.
Conclusion
With the emergence of AI technology in recent years, there is widespread adoption of AI tools into cybersecurity systems, although few risks are involved. Artificial Intelligence (AI) can identify and prioritize risks and instantly spot any malware on the network to guide cybersecurity incident response.
AI enables cybersecurity teams to have powerful human-machine partnerships pushing the boundaries of knowledge and drive cybersecurity in a more significant way.
