While artificial intelligence (AI) offers many benefits, when it comes to cybersecurity, it can wreak havoc in organizations, both internally and externally. An inevitable disruption exists from the potential risks and vulnerabilities posed by AI-driven cyberattacks, from advanced evasion techniques to automated social engineering tactics. To counteract this trend, it’s imperative for cybersecurity professionals to understand how malicious actors harness AI to exploit vulnerabilities, evade detection, and launch devastating cyber assaults. This dark side of AI underscores the urgent need for robust defenses and proactive measures to mitigate the looming threats in an increasingly AI-driven cybersecurity landscape.
The current and future cybersecurity marketplace
According to Grand View Research, the global cybersecurity market is projected to grow at a 12.3 percent compound annual growth rate (CAGR) by 2030 from the estimated USD $222.66 billion in 2023. Much of this market growth is due to the increased use of smart devices, e-commerce, and the cloud. As a result, as cybersecurity threats continue to expand exponentially, perpetuating the need for advanced solutions also escalates.
Everything is digital in modern landscapes, from emails and advertisements to buying cars and trading stocks. Society’s increased dependence on digital purchases and business has led to innumerable digital identities and signatures and, as a result, more potential for security breaches. While the future is unpredictable, the need for cutting-edge security tools, stringent regulations, and forward-thinking strategies is clear.
AI becomes a bigger threat
Ten years ago, most people were unaware of what cyberthreats could really do. Only the bigger, more public cyberattacks were discussed, like massive security breaches that affected the masses or when suggestive celebrity photos were distributed without the subject’s consent. Unlike in the past, where sizable companies might see one or two cyberattacks a year, many large and small companies are now being hit by targeted bots thousands of times per minute.
Though cloud services provided by companies like Google, AWS, and Azure have strict protocols and security measures in place, none are infallible.
The expansion of cloud migration has created further opportunities for AI-driven cyberattacks. Cloud services offer a convenient way to store massive amounts of data, but many people mistakenly believe their information is 100 percent safe. Though cloud services provided by companies like Google, AWS, and Azure have strict protocols and security measures in place, none are infallible. While a person’s data is likely much safer with large companies like these, the problem arises when their security is breached, and the personal data of billions could be affected.
Ransomware infects countless individuals and organizations every year. Even companies with high security and strict guidelines are not immune because the more employees they have, the higher the chance someone might open a file with malicious software attached. This may affect the company and have significant repercussions throughout the supply chain. Cybercriminals can use AI to create bots and attack security walls faster and more efficiently than ever.
High-profile attacks can disable infrastructure or threaten governmental security. Many incidents have cost the targets time, money, and often both, along with a loss of consumer trust. In 2023, the world witnessed unprecedented ransomware attacks, with a victim count of over 55 percent higher than the previous year. Early in 2023, the LockBit ransomware group infiltrated the United Kingdom’s Royal Mail, disrupting the national infrastructure.
Northeastern U.S.-based Harvard Pilgrim Health Care was the victim of an incident in April 2023, losing sensitive data—like names, addresses, and social security numbers—for over 2.5 million people. In a positive turn of events, The City of Oregon suffered major network disruption by a sophisticated ransomware attack, but due to their foresight in investing in backup tech, they restored the system without paying any ransom.
Cybercriminals are miles ahead of the general public in learning, developing, and weaponizing AI. The typical individual has only just begun to pay attention to AI and its capabilities, but malicious actors have used this tech for years to create advanced attacks. A person can prompt AI programs like ChatGPT to write lines of code, and once it does, this code might be used to target security software. AI is simplifying the sending out of viruses, malware, ransomware, and more by automating it.
The costs associated with cybersecurity can be high, but not solely because of the AI models themselves. Various compliance requirements, like the Health Insurance Portability and Accountability Act (HIPAA), might necessitate additional funding, but the fines resulting from non-compliance are even higher. A ransomware attack could cost organizations tens of millions of dollars or more, but the ransom fee isn’t the end. The resulting loss of customer confidence and loyalty is a long-term effect that some never recover from, leading to bankruptcy.
Fighting AI with AI
New cyberthreats are invented regularly, and previous threats are constantly evolving, especially with the aid of AI models. So, if malicious actors use AI to perfect their cyberattacks, why wouldn’t organizations use AI to perfect their cyber defenses?
The key to fighting cyberattacks is taking a proactive rather than reactive approach to prevention and mitigation. Advanced AI-powered threat detection software can streamline the process, allowing companies to stay on top of ever-evolving threats. These programs can run endlessly in the background without supervision, and they self-learn, meaning the solutions evolve with the cyberthreats.
With behavioral and predictive analytics, the AI model uses machine learning (ML) techniques to determine risks and devise solutions.
AI’s ability to analyze big data in a fraction of the time humans can make these models crucial for cybersecurity. A properly trained and programmed AI system sorts the large data sets and identifies patterns humans may miss, allowing it to detect anomalous activity. It can then find potential security vulnerabilities to be addressed. Once these issues are patched, all that remains is monitoring activity and remaining vigilant about new threats.
With behavioral and predictive analytics, the AI model uses machine learning (ML) techniques to determine risks and devise solutions. It continually takes in new data at lightning speed, increasing the AI’s ability to mitigate attack vectors. And the entire process can be automated, reducing time, money, and other resources that might be required. AI automation can also aid in stronger access management, more robust encryptions, round-the-clock network monitoring, and faster incident responses.
Effective implementation strategy
AI’s explosion has left many organizations scrambling to incorporate the technology into their plans, lest they fall behind their peers or competition. This unexpected distraction interrupted business flows, slowing progress and potentially leaving room for additional cybersecurity risks. A successful implementation will increase cost savings and reduce time expenditure. Here are the steps to develop a clear, practical, and specific strategy.
- Establish goals and use cases. Without specific, measurable, relevant goals and actionable steps to achieve them, an implementation plan will either take more time and money to accomplish or might fail altogether. Assess needs, then define objectives, which steps to take, and who is responsible for each item.
- Create an expert team. Gather a group of cybersecurity experts familiar with AI models and their capabilities, common cyberthreats, and the organization’s current infrastructure.
- Conduct data analysis. Perform data and process audits to assess where these stand and exactly where and when improvements can be made to achieve previously communicated
- Research and select appropriate tech options. Research the various types of cyber tools with inbuilt AI technology that can improve cybersecurity before selecting the best tools for the job. ML is the most common, but deep learning is more advanced, and natural language processing (NLP) is a good option for text-based channels.
- Test and train. Once the AI model is chosen, running test scenarios can further solidify it as the right option or show that different technology is needed. When the AI program is put into full effect, everyone interacting with it should already be trained in its uses and best practices.
- Monitor and adapt. Constant monitoring, regular audits, and continued training are critical to staying ahead of problems. Decision makers and cybersecurity teams who remain flexible and adaptable continually look for areas of improvement and adjust strategies as needed.
Mistakes and mitigation
When implementing AI into cybersecurity strategies, one of the biggest mistakes is using insufficient, low-quality data that lacks diversity. Improper testing and failing to ensure varied datasets can lead to limited or biased results, leading to potential areas for security breaches when anomalous behavioral patterns cannot be recognized. Black box AI models lack transparency in how they arrive at conclusions, making troubleshooting these problems nearly impossible. Doing thorough research in advance, having at least one expert in the field, and running sufficient applicable testing reduce or eliminate such mistakes.
Regulations
ChatGPT and other AI programs recently opened the door wider than ever to security attacks. Cybercriminals leverage the lack of regulations in the AI space to get away with malicious activities, and local, state, and national governments struggle to keep up with laws that better protect individuals and organizations. It’s challenging for users to plan AI tech strategies without knowing what legislation might emerge that could upend everything.
In the United States, federal response is slow, held up by bureaucratic red tape. Further, the frameworks that have come out were voluntary guidelines, not mandates. State and local governments sometimes move faster, and many bills are in play, with few having passed. This makes consistency difficult for organizations spread nationwide, costing time and money to monitor state-by-state changes. Many AI businesses like OpenAI, the founders of ChatGPT, are burdened by expensive litigation due to a lack of regulations, and the outcomes of these lawsuits will have widespread effects on everyone who uses AI technology.
The Gitnux Marketdata Report 2024 shows that 88 percent of cybersecurity professionals feel efficient security task performance will rely on AI in the upcoming landscape. Cybercriminals are gaining a better understanding of AI’s capabilities and are developing new ways to conduct cyberattacks. Greater advanced threat detection, more accurate predictive analytics, and deception technologies—like decoy networks—will be crucial to building effective cyber defenses. It’s not too late to get started with an AI implementation strategy. Those who do not take heed and proactively implement AI into their cybersecurity systems are inviting in malicious intruders, from which there may be no recovery. 
FNU Divyanka
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