Number 5: Model Abuse
Model abuse is when an AI system's intended functions are used for harmful purposes. Think of it less like hacking a system and more like tricking a powerful tool into doing bad things. This could involve using a generative AI to create convincing phishing
emails at scale, write malware, or generate disinformation for social engineering campaigns. The model itself isn't broken; it's working exactly as designed, but for malicious ends. This risk is ranked here because while the consequences are serious—from spreading misinformation to enabling cyberattacks—it often relies on other attack vectors to succeed. Defending against it requires not only securing the AI but also maintaining strong, traditional cybersecurity hygiene.
Number 4: Deepfakes
Deepfakes—hyper-realistic video or audio generated by AI—have moved from a novelty to a potent business threat. Cybercriminals now use AI-generated voice and video to impersonate executives and trick employees into making fraudulent wire transfers, sometimes costing companies millions. A recent survey revealed that nearly three-quarters of security leaders believe their organization has faced a suspected deepfake attack in the last year. These attacks exploit the most human of signals: a familiar face or voice, turning our trust into an attack surface. The technology is becoming increasingly accessible, allowing attackers to create convincing fakes with minimal source material, making it a growing threat for fraud and reputation damage.
Number 3: Shadow AI
One of the most widespread but invisible risks is 'Shadow AI.' This refers to employees using AI tools and applications without the knowledge or approval of their IT and security departments. An employee might paste sensitive company data into a public chatbot to summarize a report, or a developer might use an unvetted AI coding assistant, inadvertently sending proprietary code to a third-party server. While born from a desire for productivity, Shadow AI creates massive security blind spots. It opens the door to data leakage, compliance violations, and other attacks because the security team has no visibility into these tools. Our research shows a huge number of employees use unsanctioned AI, sharing everything from financial data to employee records.
Number 2: Data Leakage
AI data leakage happens when sensitive information is unintentionally exposed through an AI system's normal operation. This doesn't necessarily involve a malicious hack. It can happen when an employee pastes confidential information into a prompt, or when an AI model memorizes and later reproduces sensitive data from its training set. A single misconfigured cloud storage link once led to the accidental exposure of 38 terabytes of private Microsoft data, including passwords and keys. Because enterprise AI systems often connect to internal documents, customer records, and financial data, a poorly designed query or an overzealous AI agent can expose vast amounts of information, creating enormous regulatory and reputational risk.
Number 1: Prompt Injection
Ranked as the top risk by the Open Web Application Security Project (OWASP), prompt injection is an attack that hijacks an AI model by feeding it malicious instructions disguised as legitimate input. An attacker can trick the model into ignoring its safety protocols to reveal confidential information, execute unauthorized commands, or spread misinformation. Think of it like social engineering for AI. For example, a cleverly worded prompt could command a customer service bot to reveal another user's private data. This vulnerability is number one because it exploits a fundamental aspect of how language models work—they don't easily distinguish between a developer's original instructions and a user's input, making it a simple but powerful way to cause chaos.
