AI is proving to be one of the significant game-changers in the business world. As more and more organizations are going digital, many of them are engineering new ways to implement AI-based functions into every platform or software tool in their possession.
Expectedly cybersecurity industry is also implementing AI-based platforms and tools to accelerate their performance. As a natural result, cybercriminals are also fashioning out new ways to attack organizations.
AI is evolving the cybersecurity landscape, and through this “Advantages of AI in cybersecurity” article, you will learn how.
Artificial intelligence is a field related to computer science that involves creating systems to perform tasks that generally require human-like intelligence like:
There are mainly three types of artificial intelligence, namely:
Much faster detection and response to threats compared to other traditional methods, providing very few opportunities for attackers to improve an organization’s security posture.
AI can be trained to recognize patterns more accurately, thus reducing false positives and helping the security team focus more on resolving issues that genuinely represent real threats, rather than wasting time rectifying incorrect alerts.
AI can precisely identify and categorize threats with the help of machine learning algorithms, providing advantages like an increase in the accuracy of threat detection and less manual intervention, increasing the efficiency of security operations.
The scalability feature of AI-driven systems can handle the complexities of growing data without the proportional increase in resources, thus significantly resulting in cost savings.
Considers historical data to identify and predict future threats, which helps an organization create a mitigation plan before the risks materialize
Helps speed up the action of identifying threats and vulnerabilities before they are exploited by the attackers thus strengthening defenses and preventing attacks.
Its capacity to handle enormous datasets efficiently makes it possible to detect threats that can be otherwise missed by human analysts due to the sheer volume of information.
capacity to identify unusual patterns and behaviors even if those threats were neither encountered nor documented.
AI models continuously learn from new data to adapt to emerging threats, thus helping to keep defenses up-to-date from the ever-evolving cyberattack techniques.
Detects an anomaly, which generally consists of analyzing the deviation from the normal behavior caused by security threats like insider attacks or compromised accounts.
1. Criminals using AI to launch attacks: Cybercriminals are using it to become tech-savvy & enhance their attack making them more sophisticated and harder to detect.
2. Lack of human judgment: There is a lack of human touch, such as understanding & contextual awareness due to which it can miss subtle and even complex threats.
3. Possibility of false positives: Benign activities might be identified as threats leading to unnecessary alerts and the disruption of legitimate activities.
4. Not able to adjust to new threats: Without continuous learning & proper training, it won’t be able to adapt to the emerging trends and latest threats.
5. Ethical dilemmas & considerations: It might make you ask ethical questions like, how one can balance security with individual freedom.
6. Privacy Concerns: AI systems cannot work to their fullest until they can access a large amount of data, which can raise some privacy issues.
7. Increase in dependence on AI: Being overly dependent on the AI can create vulnerabilities in case of system failure or if they get compromised.
8. Implementation costs: New companies won’t be able to bear the implementation costs of ongoing maintenance, technology & training into their current cybersecurity infrastructure.
9. Potential for bias: You might need to deal with biased results if the data they are trained upon is biased or incomplete.
Yes, undoubtedly yes! Perhaps AI is nearly the future of every industry, leave alone the cybersecurity industry. According to CyberCrime magazine, $10.5 trillion will be the cost the world will pay for cybercrimes by 2025. According to Statista, 2021 alone witnessed 68% AI-enabled cyberattacks. As per the survey, in the near future, AI can be used to enhance ransomware attacks which could put a lot of danger on the security of many companies.
The numbers mentioned above clearly state that cybercriminals are not going to set back from achieving their malicious goals; instead, cybersecurity professionals have to whisk their strategies. Cybercriminals are using AI to evolve their strategies to attack more effectively at a rapid pace. As a result, many companies have to use AI as a part of their cybersecurity strategies.
More and more cyber resilience strategies are adopting AI; subsequently, it has become vital for business leaders to leverage agile protection in this dynamic threat landscape. Innovations like Cybergraph are promising evidence that AI will offer favorable value to cybersecurity. Along with it, will be needed.
AI is here to stay, and it will remain for long. However, it is not a silver bullet, but it will surely reshape the future of cybersecurity.
In many regions worldwide, especially the MEA area, many businesses are using AI surveillance. Fighting against AI-driven cyber-attacks can be the next big step to play your defense. Here are a few thoughts that you can consider.
Threat hunting services include the practice of proactively looking for the cyber threats that may be lurking undetected in the network. A powerful data analysis and MI sift through huge amounts of information in order to detect potential threats. Threat hunting services are really effective for fighting against AI-powered cybercrimes.
It is crucial to analyze the software code for bugs, behavioral anomalies, and malware; therefore, running regular scans is not enough. The cybercriminals will use the previously unknown tools and techniques to ensure they crack the code. Therefore, understanding the risks inside the code is important.
To fight against AI, using AI. While monitoring your logs, use Machine learning security log analysis to search for the patterns. Looking for the patterns will give you protective measures that you can incorporate into the security strategies.
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