What is AI Security?

The potential of artificial intelligence to revolutionize the field of cybersecurity is clearly promising. But there are a growing number of AI security risks and challenges that organizations need to consider when implementing an effective AI security strategy. These include:

• Risk of an increased attack surface—Integrating proprietary and third-party AI models into an organization’s IT infrastructure can dramatically expand its attack surface, increase the number of weak points bad actors can exploit, and leave AI systems vulnerable to more powerful or frequent cyberattacks.
• Risk of malicious or adversarial attacks on AI data and algorithms—Bad actors can gain unauthorized access to AI tools and data, manipulate AI data and algorithms to insert gaps or biases, jeopardize the ability of AI models to make accurate predictions or defend against future attacks, and manipulate, misuse, or steal AI models and algorithms.
• Risk of data poisoning, corruption, and manipulation—Cybercriminals can corrupt machine learning (ML) pipelines or “poison” AI models by deliberately changing their input data or inserting new data of their own to compromise AI tools and systems.
• Risks to AI training models—Cybercriminals can steal, manipulate, or reverse-engineer proprietary AI training models. They can also corrupt, breach, or tamper with the data used to develop and train AI models to compromise their accuracy, effectiveness, and integrity.
• Risk of bias, discrimination, data privacy, and a lack of transparency—Human error and cyberattacks can increase data privacy concerns and bias in AI models and potentially compromise principles of transparency, fairness, and accountability.
• Risk of non-compliance with industry and government regulations—Failure to protect sensitive, personal, or confidential data can result in heavy penalties and charges of non-compliance from both government and industry regulators, including the General Data Protection Regulation (GDPR).
• Risk of attacks on third-party suppliers—Attackers can target AI systems anywhere along an organization’s supply chain to take advantage of weaknesses in the AI networks, components, frameworks, and software libraries of third-party partners, and then use those vulnerabilities to target other AI models anywhere else along the chain.
• Risks of AI model drift and decay—Over time, all AI models can be vulnerable to drift or decay. Bad actors can exploit decayed or drifting AI models to alter their behavior or compromise their accuracy and reliability.
• Risk of chatbot credential theft—Cybercriminals can steal chatbot credentials for AI providers like ChatGPT or buy stolen credentials on the dark web and use them to gain illegal access to proprietary AI tools and systems.
• Risk of deepfake photos, videos, and audio—Bad actors can use AI-generated deepfake images and videos to defraud organizations, extort individuals or companies, trick employees into granting access to critical systems, disclosing confidential information, or stealing valuable data.
• Risk of direct and indirect prompt injection attacks—Prompt injection attacks can use malicious code to trick large language models (LLMs) and other AI tools into leaking sensitive information, permitting unauthorized access, or deleting key documents.
• Risk of AI hallucination abuse—Bad actors can take advantage of common AI “hallucinations” to deliberately compromise the data AI models rely on and the decisions they make.
• Risks to cloud assets and infrastructure—Cybercriminals can hack into cloud-based AI models and corrupt or steal their data and other assets.

If organizations don’t make sure their AI security and cybersecurity measures are as robust, comprehensive, and up to date as possible, bad actors can exploit these and other risks to undermine the effectiveness and reliability of AI models, steal sensitive or private data, and potentially cause significant financial and reputational harm.