Artificial intelligence is embedded in everyday life. It powers business operations, public services, and critical infrastructure. From predictive analytics to automated decision-making, organizations rely on AI to drive innovation, efficiency, and growth.
As AI adoption accelerates, so do the risks. Securing artificial intelligence is essential and helps ensure that AI systems remain trustworthy, resilient, and safe for both public and private sector use.
These are some of the security challenges AI systems introduce:
Prompt Injections
As explained in our white paper, Prompt Injections: The Inherent Threat to Generative AI, prompt injections are an attack technique that manipulates large language models (LLM) and their task-specific agents to engage in malicious behavior.
There are two types of project injections. Direct prompt injections occur when an attacker directly passes input to the LLM to manipulate its behavior and bypass or overwrite safeguards. Indirect prompt injections occur when attackers inject malicious prompts in the model through an intermediary step, such as prompts embedded into external data sources that pass from AI agents to an LLM.
Data Privacy
Data privacy is not a new security challenge, but it takes on a new dimension with AI. Organizations may train publicly available AI models on personally identifiable information (PII), trade secrets, and other sensitive details. Those same models may remember that information and regurgitate it for other users, creating business and reputation risks.
Data Poisoning
Threat actors may feed corrupted, untrue, or misleading information to the training or tuning inputs of AI models. These types of training sets will "poison" the outputs of those models, raising the possibility of errors in relation to finance, market research, compliance, and other business-critical functions.
Without a strong security foundation, these and other AI risks can undermine trust, disrupt operations, and expose organizations to significant harm.
Securing artificial intelligence is essential to ensuring that AI systems remain trustworthy, resilient, and safe for both public and private sector use. A risk-based approach to AI security helps organizations protect data, maintain system integrity, and support responsible AI adoption at scale.
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