In today’s dynamic business environment, artificial intelligence (AI) is increasingly adopted, yet many companies remain unaware of its associated risks, according to industry analysts.
At present, the issue of ‘AI data/model poisoning’ has emerged as a significant threat. This situation arises when malicious entities intentionally alter the training data of an AI or machine learning model, which undermines its reliability.
This type of attack primarily targets predictive or narrow AI solutions—those that are designed for specific tasks—during the MLOps cycle, which involves the deployment of machine learning models. In generative AI systems, this type of data poisoning is more likely to manifest within Retrieval-Augmented Generation (RAG) and knowledge graphs, instead of at the model level. The most alarming concern relates to Agentic AI, where such poisoning not only distorts outputs but also affects autonomous operations.
To illustrate, imagine an oil and gas firm utilizing AI for predictive maintenance. If a cybercriminal inputs fraudulent sensor data into its training set, the AI could fail to recognize legitimate warning signs or mistakenly identify operational equipment as faulty. This could result in unplanned shutdowns, expensive repairs, and potential safety risks.
Similarly, in the finance sector, tainted stock data within a bank’s investment agent’s knowledge graph could lead to poor investment decisions by the agentic AI, ultimately incurring substantial losses for clients. “AI poisoning jeopardizes the accuracy of AI-driven decisions, leaving businesses open to operational failures and financial setbacks. This underscores the need to safeguard data integrity to maintain the safe and efficient management of resources,” stated Premchand Kurup, CEO of Paramount, in a recent interview.
Data poisoning poses a severe risk to businesses, especially in sectors that require high sensitivity, like finance and cybersecurity. For example, in a bank’s AI-based fraud detection system, if attackers corrupt the training data, the system might overlook actual fraud cases, leading to significant financial repercussions. Likewise, in cybersecurity, a tainted malware detection system could misidentify threats as harmless, exposing systems to attacks. The fallout can extend beyond immediate financial losses, as data poisoning can damage customer trust and harm reputations. “Addressing these sophisticated threats necessitates a solid AI cybersecurity framework and protective measures. Any shortcomings in these areas may diminish organizations’ confidence in AI initiatives,” added Kurup.
As the integration of AI speeds up, companies are encouraged to establish a comprehensive AI Framework for Cybersecurity to ensure responsible and secure use of the technology, as Kurup highlighted. “The initial element of this framework is AI governance, which outlines clear standards for the ethical use of AI, addressing data privacy issues and legal responsibilities while enhancing productivity. The second aspect focuses on securing AI systems to shield AI models from external threats across the complete AI lifecycle—from data collection to deployment and retirement. This enhances AI integrity and resilience by minimizing exploitation risks,” he elaborated.
The third component involves deploying AI in cybersecurity processes. For instance, incorporating AI into Security Operations Centers (SOCs) can boost threat detection and response capabilities. Additionally, utilizing Agentic AI for managing identity and access can help in mitigating risks proactively. Last but not least, ensuring data protection during its transit between various systems is crucial for stopping unauthorized access. “At Paramount, we are dedicated to establishing an effective framework across essential cybersecurity domains, including Network Security, Identity and Access Management, Cloud Security, Data Security, AI in Cybersecurity, and AI Adoption Framework,” Kurup noted.
Although no specific incidents of AI poisoning have been reported in the Gulf Cooperation Council (GCC) region, the rising technological advancements indicate an increasing awareness of cybersecurity. The region has transitioned from merely following international trends to actively adopting AI technologies.
Generative AI is anticipated to contribute between $21 billion to $35 billion annually to GCC economies, adding to the existing $150 billion figure from other AI technologies. This could account for approximately 1.7% to 2.8% of the current yearly non-oil GDP in the GCC. A recent survey by McKinsey highlighted that nearly three-quarters of participants indicated their organizations are already leveraging generative AI in at least one business operation, with over 50% of GCC respondents allocating at least 5% of their digital budgets to generative AI—surpassing the global average of 33%. Despite the notable uptake, detailed reports on AI-related threats remain scarce due to the nascent adoption stage and concerns regarding reputational risks.
Nevertheless, the GCC’s status as a significant global economic player, combined with existing geopolitical issues, necessitates a strong focus on security. “Given that global tech giants like Apple and Amazon have faced AI poisoning, the region must enforce stringent security protocols to protect AI systems and their considerable economic contributions,” Kurup stated.
AI poisoning influences more than just financial aspects—it can also endanger a company’s brand image, with varying impacts across industries. For example, in an oil and gas organization, a compromised predictive maintenance tool that fails to detect equipment malfunctions can result in expensive downtimes and repairs. In the energy sector, the cost of downtime can be approximately $2.48 million for each hour lost. Fortune Global 500 companies encounter average costs of around $129 million annually for unplanned downtimes per facility.
“While it remains challenging to quantify specific financial losses due to AI’s evolving landscape, the risk of substantial economic repercussions is clear. As AI gets more integrated into critical infrastructures and business operations, the financial fallout from successful AI poisoning attacks is expected to become more severe,” Kurup concluded.