- Data Poisoning: AI models can be manipulated by introducing false or misleading data during training. This can affect the agent’s decision-making process and potentially cause it to behave maliciously or incorrectly.
- Adversarial Attacks: These involve feeding the AI agent carefully crafted inputs designed to deceive or confuse it. In some cases, adversarial attacks can make an AI model misinterpret data, leading to harmful decisions.
- Social Engineering: Scammers might exploit human interaction with AI agents to trick users into revealing personal information or money. For example, if an AI agent interacts with customers, a scammer could manipulate it to act in ways that defraud users.
- Security Vulnerabilities: If AI agents are connected to larger systems or the internet, they can be hacked through security flaws, enabling malicious actors to gain control over them. This can be particularly concerning in areas like financial services, autonomous vehicles, or personal assistants.
Conversely, if the agents are well-designed and governed, their very AI’s autonomy could be used to enable adaptive security, allowing them to identify and respond to threats.
Gartner’s Litan pointed to emerging solutions, called “guardian agents” — autonomous system that can oversee agents across domains. They ensure secure, trustworthy AI by monitoring, analyzing, and managing agent actions, including blocking or redirecting them to meet predefined goals.
An AI Guardian Agent governs AI applications, enforcing policies, detecting anomalies, managing risks, and ensuring compliance within an organization’s IT infrastructure, according to business consultancy EA Principles.
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