Data Validation Faces New Cyber Threats From Artificial Intelligence

Please welcome Lucy Roberts, Head of Publishing at Ocere Ltd. This is her first contribution to the 21st Century Tech Blog. Lucy started her career as a content writer and still finds opportunities to produce articles like the one that follows. In it, Lucy describes data validation and the growing cyber threat by bad actors on the Internet who are now being fuelled by artificial intelligence.

If unfamiliar with data validation, the term refers to checking for the accuracy, completeness, and consistency of data to ensure it is correct and useful for an intended purpose.

We are witnessing an increasing rise in misinformation in the digital realm that often appears as “truth.” Ensuring facts are facts and not fake, therefore, is important. I’ll let Lucy explain the rest.

Data validation is now a fundamental security requirement for companies and organizations increasingly dependent on digital transactions. Conventional data validation concentrates on accuracy, error prevention, and compliance maintenance. Not anymore. Cyber threats, especially those driven by artificial intelligence (AI) are becoming more common. Deepfake data manipulation, synthetic identity fraud, and automated penetration testing by hostile actors are just a few of the new dangers from AI-driven attacks. Therefore, companies are developing more sophisticated validation methods to guard against AI-based cyber threat risks.   

Financial transactions face AI-driven escalating risk. Invoicing, billing, and order processing are critical to business operations. Threats range from hyperrealistic phishing campaigns and deepfake impersonations that use voice-cloning tools.

Cybercriminals use AI to do model evasion where transaction data is modified without being detected. For companies using counteracting AI tools, cybercriminals rely on AI algorithms to corrupt the training sets and compromise risk assessments.

Increasingly, maintaining the integrity of procedures like order-to-cash reconciliation has become more important for fraud prevention than just a question of efficiency. Using AI, cyberattacks falsify payment records, tamper with transactional data, and use differences in reconciliation processes to get beyond conventional security systems.

Because of this, real-time AI-powered data validation systems using machine learning are needed to find and deal with problems before they cause financial harm. 

Ignore The Threat At Your Peril

Most companies fail to disclose details about cyberattacks for reputational reasons. Many continue to ignore the threat until they become its victims. For some, the stories have leaked out including the ones that follow:

• A number of U.S. banks and credit unions in 2021 were subjected to cyber attacks where synthetic identities were used to steal hundreds of millions of dollars.
• An unnamed American bank in 2022 experienced an adversarial attack on its own AI-based fraud detection system. The cybercriminals manipulated transaction data using machine learning allowing them to post multiple fraudulent transactions causing the bank to lose $1 million.
• A cryptocurrency exchange lost $35 million in cryptocurrency when attacked by cybercrooks who used advanced data analysis and targeting plus highly sophisticated social engineering to overcome established threat detection processes.
• An Australian company was a victim of an AI-enhanced ransomware attack that cost it $30 million.
• A Japanese company was attacked by cybercriminals who used social engineering and AI-driven data analysis to impersonate executives directing employees to do money transfers causing it to lose $37 million.

AI Threats That Affect Data Integrity   

Cybercriminals use fake datasets to fool validation systems. AI-generated hazards include fake consumer profiles, synthetic transactions, and financial entries that match genuine data. This condition causes typical rule-based validation systems to struggle to distinguish what is real from what is not. These malevolent AIs bypass CAPTCHA, exploit automated system flaws, and produce data discrepancies that disrupt operations.

Machine Learning Real-Time Detection Blocks Attackers   

Countering cyberattacks that involve fake data sets, businesses are using machine learning algorithms to detect suspicious patterns in massive data sets. The systems use predictive modelling, behavioural analysis, and anomaly detection to find potentially dangerous data entries on the fly before a company’s security gets compromised. Dynamically validating data instead of using guidelines improves AI-powered assault protection.   

Enhancing Blockchain and AI Data Validation   

Companies also use AI-driven analytics to validate data and neutralize attacks. AI-boosted data validation learns from historical trends and improves accuracy, preventing attackers from exploiting security flaws. Neural networks and deep learning algorithms are used to automate complex data verification processes, reducing manual control and improving fraud detection.   

Blockchain, the distributed ledger that underlies cryptocurrency transactions, is being used to provide an unchangeable, open ledger for essential data and improving validation. Blockchain ensures authorized and recorded data whether for finance, healthcare, or supply chains cannot be modified and dramatically reduces AI-generated data manipulation and forgeries boosting digital transaction confidence.

Combining AI analytics and blockchain is a multilayer defence against traditional and AI-based cyberattacks.   

The Future of Data Validation In An AI-Dominated Landscape   

As AI impacts cybersecurity, organizations must proactively alter data validation methods. Companies that neglect these changes risk operational disruptions, financial fraud, and data breaches. AI-driven validation models, adaptive threat detection, and distributed security will become industry standards for data protection.

Investment in AI-driven data validation solutions is increasingly mandatory. Combining machine learning algorithms, behavioural analytics, and blockchain technology can help companies avoid AI-generated dangers and protect data in a hostile digital world. Increasingly, companies are deploying layered defences to block these increasingly sophisticated AI-driven attacks. It is becoming a cyberwar between AIs.