In the early days of ransomware, cybercriminals employed crude methods, including poorly written phishing messages, smishing messages, and spam emails, to block victims from accessing their systems or the content of their systems and demand payment. While damaging, these attacks were relatively easy to detect and stop with proper training and security tools. However, in recent years, the threat has taken a sharp turn. Artificial Intelligence (AI) and machine learning, once heralded as the silver bullets of cybersecurity, are now being hijacked by cybercriminals to make ransomware attacks faster, more targeted, and far more convincing.
This development is more than just an advancement in malicious software techniques, but also represents a shift in the entire threat landscape and serves as a significant warning about the unintended consequences of AI misrepresentation, data ethics failures, and governance blind spots. It therefore implies that the same technologies we trust to protect us can become the very tools that can threaten our digital lives if not applied correctly.
In cybersecurity, AI has been a game-changer, as it is fundamentally neutral and its behavior is shaped by the data it learns from and the goals it is trained to pursue. Tools powered by machine learning detect anomalies, flag suspicious behavior, and automate threat responses in ways that human analysts cannot match.
However, when an AI system is not adequately protected, it becomes a tool that can be manipulated by threat actors, who have learned to turn AI’s strengths into vulnerabilities. For instance, a major multinational corporation lost over $25 million in 2013 after one of its employees was deceived into transferring funds following a video call, which was believed to have originated from the organization’s Chief Finance Officer (CFO). Although the call appears to be genuine, deepfake technology adopted by the malicious actors was generated by AI, part of a sophisticated ransomware extortion campaign. Despite the deepfakes appearing highly realistic, the videos or voice recordings generated were not.
In another example, AI-generated phishing emails adapt in real-time to user behaviour, language patterns, and emotional cues. These are far more persuasive than the generic “Nigerian prince” scams of the past. They are context-aware, grammatically correct, and nearly impossible for the average employee to detect. When employees are tricked into clicking malicious links or opening attachments, ransomware quickly spreads, encrypting files and crippling operations.
Misrepresentation is one of the major causes of the growing threat in AI. This is the use of AI in a manner that appears trustworthy but is fundamentally misleading. AI tools are often promoted as infallible, objective, and capable of replacing human judgment. When used in cybersecurity systems, a false sense of security can develop among users and executives alike; as a result, AI can be manipulated, biased, or flawed. Data bias is one algorithmic bias that results when the data used in training an AI system is unrepresentative of the broader population or contains historical prejudices, leading the AI model to perpetuate or even amplify these biases during inference. For example, suppose a recruitment AI tool is trained primarily on data from previous employees that reflects historic industry demographics. In that case, the tool may unfairly disadvantage minority applicants due to this unbalanced representation. Ordinarily and in most cases, AI models inherit biases from historical data, which can skew results against underrepresented groups.
This is especially dangerous in ransomware defense. AI systems trained on outdated or limited data may fail to detect novel threats. Some organizations deploy “black box” AI tools with no visibility into how decisions are made, only to later discover that the system failed to raise the alarm during a malicious breach.
Attackers test and tune their ransomware by bypassing AI-based detection, feeding adversarial inputs that trick the algorithms, thereby exploiting this blind spot. Meanwhile, the internal teams assume the AI systems are secure because the outcome of the AI systems reports no anomalies or issues.
It is worth noting that every AI model is only as good as the data on which it is trained. However, in the cause of quickly adopting AI systems, many organizations often neglect data ethics, the principles that ensure data is collected, used, and protected responsibly.
Consequently, some of these resulted from the poor data practices:
- Exposure of personal data used in social engineering attacks.
- Inclusion of sensitive information in public AI datasets.
- Training AI systems on biased or invalid data can reduce the accuracy of expected outcomes and introduce vulnerabilities.
For instance, a company that uses customer service chat logs to train its internal AI assistant. If those logs contain confidential information or identifiable customer data and are not properly anonymized, they become high-value targets for ransomware actors. This singular breach could lead to enterprise system shutdowns, along with threats to expose sensitive information unless a ransom is paid.
Moreover, the lack of ethical review surrounding AI projects means that these security implications could go unnoticed, as AI system developers focus on performance and cost savings without considering what can be done with the data if it is accessed and stolen by attackers.
The rise of AI-powered ransomware has revealed deeper structural problems when AI or network infrastructures fail, and no one is accountable. This is because, despite the advancement in threat strategies by malicious attackers, most organizations still treat cybersecurity as an IT issue rather than a governance imperative. Some of the consequences for organizations without strong AI governance are highlighted below.
- Absence of policies that govern the proper implementation and adoption of how AI systems should be secured.
- The cybersecurity teams may not understand how AI tools function or fail.
- Underestimating AI risk by the top management of organizations.
The implication of the above narrative leads to critical delays during ransomware incidents within organizations. As a result, IT security teams struggle to respond appropriately, legal departments are uncertain about breach notification requirements, and organizational leadership is thrown into confusion.
To close this gap, organizations must embed AI and data governance into their enterprise risk management frameworks in the same manner as they prioritize financial, legal, and operational risks.
The impact of ransomware transcends locking or denying access to organization information systems or financial loss, but could also involve loss of lives. For instance, a ransomware attack could disrupt hospitals, shut down schools, and threaten national infrastructure. The 2022 ransomware attack at the Los Angeles Unified School District, where students’ data were compromised and the institution’s internal systems were shut down, is an example. Investigations revealed weak authentication protocols and a lack of AI oversight in the school security infrastructure.
In the UK, the NHS has faced increasing ransomware threats, some potentially exacerbated by poorly governed AI diagnostic systems. In some cases, AI tools inadvertently increased network exposure by connecting outdated equipment to central systems. The consequences of such breaches can have a negative impact on different industries, such as surgeries and appointments cancellations, delays in service delivery, privacy violations, and loss of lives in some extreme cases.
To reduce the risk of AI-enabled ransomware attacks, organizations must act across three key dimensions:
- AI Transparency and Auditability: This is to ensure that the AI systems are explainable and regularly audited for accuracy and bias. Therefore, the deployments of “black boxes” that make risk assessment impossible should be avoided at all costs.
- Ethical Data Governance: Organizations must develop policies which would provide guidance on data collection, labelling, storage, and sharing for AI training purposes. Additionally, regular testing of datasets for privacy risks and removing unnecessary personal information must be carried out periodically..
- Leadership Commitment: The leadership makes AI governance a priority at the top. The board and C-suite should receive regular briefings on AI risks, model performance, and cybersecurity readiness. Cyber and AI risk must be part of enterprise risk dashboards—not just technical reports.
Organizational leadership, such as executive boards and C-suite leaders, must prioritize and should be routinely briefed on AI-related development, risks, model performance metrics, and cybersecurity preparedness. Furthermore, AI and cybersecurity risks should be integrated into the organization’s risk management dashboard instead of being confined to technical or operational reporting channels.
While the interdependence between AI and cybersecurity is becoming increasingly apparent, the gains and opportunities that AI presents with cyber defense mechanisms can easily be eroded by unchecked and ungoverned AI deployment. Although AI has enormous potential to transform cybersecurity, it is essential to note that ethical and governance responsibilities should not be jeopardized or neglected. Consequently, organizations that have deployed or are interested in deploying AI for their productivity, detection, and response to cyber-attacks must ensure that the concepts of fairness, privacy, trust, and transparency are equally adopted and embedded into the AI solutions in conformity with global regulatory frameworks.
Therefore, the absence of robust oversight, ethical frameworks, and operational transparency can be exploited by malicious attackers to repurpose AI technologies for malicious purposes. The recent increase in AI-related ransomware attacks underscores that these challenges transcend technical issues and also reflect deficiencies in governance structures, ethical accountability, and public trust.
Finally, as enterprises are faced with the emerging risks and societal expectations accompanied by AI systems, embracing ethical governance will position them to lead in an AI-driven economy. Otherwise, we risk living in a world where the line between innovation and exploitation becomes dangerously thin and where AI does not just detect the malicious attackers but becomes the object of exploitation by the attackers.
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