Comparing Emerging Artificial Intelligence (AI) Tools for live, work and play applications and AI tools that stand tall to defend from a Cybersecurity Breach

This paper will address the measures that can be used to compare the latest and emerging artificial Intelligence (AI) Tools that are trending for use in live, work and play applications. Specifically, this paper will discuss the AI tools that are currently used in the following areas: manufacturing, supply chain, education, logistics, entertainment, sports, healthcare, and real estate. And finally, this paper will address which AI tools can support the cybersecurity challenges that are faced in these areas and what precautions to take to ensure these AI tools stand tall to ensure these applications are not disrupted by a cybersecurity breach. We will discuss the use of NIST AI RMF and the NIST Generative AI Profile for governance, risk mapping, testing, and monitoring; NIST’s GenAI Profile is designed to help organizations manage trustworthy AI risks across design, deployment, and use. (NIST Publications). For generative AI and AI agents, we will apply the OWASP Top 10 for LLM Applications. Key risks include prompt injection, sensitive information disclosure, insecure output handling, excessive agency, model supply-chain risk, system-prompt leakage, and vector/embedding weaknesses. (OWASP Foundation) The paper will provide recommendation by comparing AI tools on business value + security maturity, not features alone. The paper will emphasize that for critical sectors such as healthcare, manufacturing, logistics, and supply chain, one should not deploy AI without identity controls, audit logs, data-governance rules, prompt-injection testing, vendor risk review, and a manual fallback process.