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Security Implications When AI Is in the Wrong HandsSecurity Implications When AI Is in the Wrong HandsSecurity Implications When AI Is in the Wrong Hands

The rapid evolution of AI has created powerful new cybersecurity threats. Organizations must respond with equally advanced defensive strategies, employee training, and regulatory compliance.

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Industry Perspectives

July 24, 2025

5 Min Read

hacker using AI technology

Alamy

By Venkata Ramanaiah Muthyala

As with many technological innovations, humans use artificial intelligence (AI) to improve the world in numerous ways, but they also use it for nefarious purposes. From deepfake audio and video impersonations of executives to AI-generated phishing emails and data poisoning attacks, cybercriminals leverage AI to exploit trust and breach systems. Intellectual property leakage, invalid ownership, social engineering attacks, and sophisticated phishing email generation are some of the most common security risks resulting from weaponized AI.

Fortunately, several effective tactics for counteracting AI misuses are emerging. Organizations can defend against these evolving threats with AI-driven detection, employee training, and other strategies.

Most Common Emerging AI-Fueled Threats

Ongoing training creates more sophisticated generative AI (GenAI) models. At the same time, misuses are more complex, stealthier, and potentially destructive to individuals, organizations, and society. Many types of proliferating unethical or criminal uses of AI include deepfakes, voice simulation, intellectual property leakage, and invalid ownership (i.e., plagiarism). Cybercriminals convincingly mimic human-created content by synthetically generating text, audio, images, and videos, known as deepfakes, that can persuade consumers. One particularly elaborate deepfake scam cost British engineering firm Arup 25ドル million.

AI is also used to automate and scale cyberattacks. Among other tactics, cybercriminals often use AI tools to perpetrate these attacks by generating malware that adapts to and circumvents security protocols. Cybercriminals can crack passwords using generative adversarial networks (GANs), enabling them to hack networks.

Increasingly, catastrophic cyberattacks that use AI as a force multiplier have five main characteristics. The first is attack automation, where there is little or no need for human intervention. Second is efficient data gathering, which allows for faster reconnaissance of potential targets, vulnerabilities, and assets than humans can perform.

The third characteristic is customization, involving data scraping public sources such as corporate websites for analysis and creating hyper-personalized and relevant messages to humans. Reinforcement learning is the fourth characteristic and empowers cybercriminals to improve their techniques or avoid detection through real-time AI algorithm adaptation.

The fifth characteristic is employee targeting. Like attack customization, employee targeting identifies high-value, potentially deceivable human targets within an organization. Deceiving individuals into believing that cybercriminals are part of a legitimate organization, making the targets more likely to assist a cyberattack unwittingly, is integral to social engineering . Perhaps the most familiar type of social engineering-based automated cyberattack is phishing, i.e., sending fake emails that request sensitive information, entice recipients to click links that trigger spyware or ransomware downloads, or direct targets to fake landing pages where they disclose proprietary organizational information used in intellectual property theft.

Data Poisoning

One type of cyberattack, data poisoning , can cause substantial harm to organizations by manipulating AI models rather than human targets. Such attacks can be particularly damaging to organizations that rely heavily on AI models for legitimate purposes — healthcare organizations and autonomous vehicle manufacturers, for example. Three of the most common types of data poisoning attacks are:

Backdoor attacks. This attack injects poisoned data so that a model behaves normally except when a specific trigger is present.
Label-flipping attacks. Training data labels are changed in label-flipping attacks to confuse the model.
Gradient manipulation. In federated learning , attackers send malicious updates to corrupt the global model behavior.

Response Strategies for AI-Powered Cyberattack Threats

Organizations can enhance their protection and resilience against AI-powered cyberattacks by developing a cybersecurity strategy for every stage of AI use. One such approach is intellectual property (IP) leakage prevention. Organizations can establish permission-based access, visibility tools, and firewall and application controls to determine where and how employees use AI tools and prevent the unauthorized transfer of company data.

Another priority is disaster recovery and incident response. AI can assist with business continuity and incident response planning by automating some phases of the business recovery plan, such as optimizing resource allocation to keep critical operations running.

Threat intelligence is the third priority. The latest GenAI tools and many existing threat intelligence and cybermonitoring systems can provide new insights regarding outputs and even detect previously missed threat alerts, increasing threat awareness and remediation efficiency in many cases.

Employee Training

Employees are pivotal frontline gatekeepers in AI cybersecurity strategies, specifically regarding social engineering threats. Key elements of an employee cybersecurity training program include creating comprehensive and easy-to-follow policies that address areas such as confidential data, password management, remote work, and regular policy updates.

It's also essential to identify data security threats such as phishing emails, insecure document transmission, and the use of public Wi-Fi. Train employees about password hygiene, such as password strength attributes, multifactor authentication, physical device security, authorized use, and remote connection protection.

Implement annual employee testing to ensure policy compliance and encourage prompt reporting of cybersecurity policy breaches.

Emerging Regulatory Protections

The European Union, the United States, and the United Kingdom have developed regulations for the ethical development and deployment of AI that offer organizations some protection against growing AI cybersecurity threats. The U.S. Cybersecurity & Infrastructure Security Agency (CISA) provides cybersecurity best practices, including AI. In addition, China has developed interim regulations, and India has a decentralized framework of policies, guidelines, and sector-specific regulations that address various aspects of AI deployment.

AI-enabled cyberattacks are continuously growing in sophistication, and the potential for societal disruption increases with greater AI adoption. The Sosafe Cybercrime Trends 2025 report indicates global cybercrime costs will increase more than 50% between 2025 and 2028 to reach nearly 14ドル trillion. Organizations can develop refined cybersecurity strategies to counteract attacks, including AI-driven detection. A central component of these strategies is improving threat detection among employees, as they are critical frontline sentinels against AI-generated cyberattacks. When companies prioritize security throughout the organization, they reduce the risk of falling victim to dangerous cybercriminal schemes.

About the Author:

Venkata Ramanaiah Muthyala is a lead system architect with 20 years of experience in the insurance, finance, technology, and healthcare industries. He specializes in designing scalable Pega applications, leading agile teams, and delivering end-to-end solutions. Venkata is skilled in system integration, workflow automation, and stakeholder collaboration, driving digital transformation and optimizing business processes. Connect with Venkata on LinkedIn .