Businesses prioritize trust and security in AI systems

Businesses need to ensure trust in their AI systems, focusing on integrity and security, alongside cost considerations. A strong AI strategy involves meeting safety, privacy, and regulatory standards. AI models, such as DeepSeek's R1 and IBM's Granite 3.2, demonstrate different approaches to AI deployment. While DeepSeek's models provide cost-effective options, they have shown vulnerabilities, especially during security tests. Notably, DeepSeek R1 was susceptible to jailbreak attacks. In contrast, IBM's Granite 3.2 claims to prioritize safety and robustness. It includes reasoning capabilities in its models, while also maintaining a strong focus on security. IBM has developed companion guardrail models, Granite Guardian, which help ensure output safety by assessing for bias and harmful content. The cost for using DeepSeek's models can be higher, at approximately $2.19 per million output tokens. In comparison, IBM's models are more economical, costing between $0.10 to $0.20 for the same amount. This makes IBM's models attractive for businesses aiming for cost efficiency without sacrificing security. When selecting AI models, businesses should consider a comprehensive risk assessment that includes security, performance, and compliance. Prioritizing these factors is crucial for successful AI implementation in enterprises. Regular evaluations and monitoring of AI systems will help mitigate risks and enhance operational integrity.