The advent of artificial intelligence (AI) and its use in the world today in cybersecurity presents a paradox. Artificial Intelligence (AI) has provided a new dimension as a Double Edged-Sword for cybersecurity. On one hand, AI offers ground-breaking tools for detecting and mitigating threats, it also holds immense potential in cybersecurity, with the ability to bolster defensive measures and mitigate threats while increasing efficiencies for threat detection. On the other, it has augmented the capability of those cyber adversaries and perpetrators, who intent to cause destruction and harm.
The Cybersecurity community welcomed the Introduction of generative AI, as this tool would act as a ‘Force Multiplier’ to enhance cybersecurity as it was seen as a means to enhance the real-time threat detection. It also was envisaged as a means to pass-on the mundane tasks for complete automation. The ability of Gen AI to carry out rapid analysis with proactive attitude was also the thought of the cybersecurity professional.
The experts of Cybersecurity, also voiced their concern of another domain of cybersecurity also being subjected to Cyberattack, by virtue of the widening of the security posture. The concern with regard to the perpetrators using Generative AI, to execute, even more sharpened techniques and tool to execute cyberattacks also loomed among the experts of cybersecurity.
AI-Powered Cyberattacks in the new Era
The weaponisation of AI cannot be just connotated as a derogatory statement. The use of AI for militarisation of cyber space, is yet a though in continuation. Yet, the use of AI by Cybercriminals and Scammers to craft unique and non-identifiable malware, email content, evasive techniques of execution is the concern here. It also has the potential of being used for widespread abuse. This then makes it obvious that these are only the beginning of what is possible. One feels that that the cybersecurity community and organisations must respond to this challenge by implementing AI-powered cybersecurity solutions to stop these attacks before they overpower the experts and evangelists. The complete technique is well available to the attacker as much it is made available to the cybersecurity experts. The recent tend of usage of AI by cyber attackers among the as below:
1. Automated Vulnerability Scanners: AI can assist the attack planner to customise a vulnerability scan using automation of enterprise network to bring out the gap.
2. Email Routing: AI-generated malicious emails are a common feature today. These are based on the behavioural analysis which is also performed using AI tools.
3. Adaptive Malware: AI can craft malware that adapts and evolves in response to the measures intended to detect or neutralize it.
4. Automated Hacking: AI systems can be programmed to carry out attacks, the ability to hunt and execute attacks at speeds unimaginable with intelligent combinations is also observed in the recent patterns.
5. Deepfakes: AI-driven algorithms can create hyper-realistic but entirely fake content. This can range from manipulated videos to voice impersonations, posing severe threats to personal and organizational security.
A SWOT Analysis on Generative AI in Cybersecurity
On a Deep Dive into the ability of AI and its usage, brings out the implications of integrating the complete ability of Generative AI. Also, we can infer that a complex landscape with intriguing potential is what the AI offers. This positivity is not in isolation and it also has a flip-side with serious risks that require thoughtful mitigation.
Strengths
• Generative AI can help improve overall cybersecurity through the means of identification of the threats and then by using the real-time preventive models provided to the AI tool.
• It can automate repetitive tasks, freeing cybersecurity professionals to focus on more complex issues.
• Generative AI can actually be used to replace the physical involvement of manpower and automate repetitive tasks such as log analysis, threat detection, and incident response. The training of this model can also enhance accuracy and speed of threat detection and response, ultimately leading to better cybersecurity outcomes.
• The capacity to handle larger chunks of data, what was humanly impossible and the use of Generative AI to analyse with an aim to identify cyberattacks and at the same time handling them with accuracy and speed, also identifying patterns and anomalies.
• AI can help organisations circumvent emerging threats by using its capability of continuous learning and adaptive methods.
Weaknesses
• Tools of Generative AI tools always comes with its associated risk.
• The chance of come with significant risks.
• The chance of false positives or negatives.
• Generative AI can make mistakes.
• Can provide biased outputs.
• It may provide different answers to the same question each time it is asked.
• The need to supply large data to be effective.
• Highly stringent process doctoring, training, fine-tuning and findings.
• Other risks include the potential for leaks of personal or sensitive information, factual errors or hallucinations, copyright infringement, and amplifying discriminatory hiring practices.
• The generative AI implementation can be expensive.
• Integrating the manual process into existing security systems, and continuously monitor and update them to ensure their effectiveness and accuracy.
• AI-powered security system could exhibit discriminatory behaviour.
• Cybercriminals can manipulate AI algorithms by feeding them with malicious data or using them to generate deepfakes, which could be used for phishing scams, identity theft, or other malicious purposes.
Opportunities
• Generative AI can be used to automate routine security tasks, such as patch management and vulnerability scanning. This frees up security teams to focus on more complex tasks, such as threat hunting and incident response, improving the overall effectiveness of the organization’s cybersecurity program.
• Generative AI can be used to improve the accuracy and efficiency of security operations. By automating tasks such as log analysis and incident triage, generative AI can reduce the workload on security teams and improve the speed and accuracy of incident response.
• Generative AI can be used to improve the overall security culture of an organization. By providing employees with personalized security training and awareness programs, generative AI can help reduce the risk of human error and improve the organization’s overall security posture.
• It can help organizations stay compliant with regulations and standards related to cybersecurity.
• Generative AI can be used to develop new cybersecurity solutions and products, creating new business opportunities.
• It can help organizations stay ahead of emerging threats and adapt to changing cybersecurity landscapes.
Threats
• The fact that the AI tools are equally available to the attacker community.
• The increasing adoption of generative AI into programming processes thereby providing a set of security risks.
• Cybercriminals may attempt to exploit vulnerabilities in generative AI algorithms.
• AI-driven code generation tools can inadvertently generate code with weak points or vulnerabilities that malicious actors could exploit.
• Biased decision making, especially in those related to cybersecurity.
Ethical Aspects of AI in Cybersecurity
The importance of Ethics is much felt when the tool is used for by individuals and organisations for un-ethical means. The use of AI to mitigate risks in cybersecurity by individuals and organisations can establish a sustainable, trustworthy, and transparent governance structure. This could involve establishing a cross-functional team to govern and help drive adoption and create opportunities to identify use cases. Good use cases, can be part of the culture of an organisation, who propose to invest in the process of adoption of AI which will inturn assist the organization to develop prototypes, and templatise the attitude.