How to Choose the Right AI Security Solution: An Enterprise Buyer's Guide | Harness Blog

AI security solutions protect LLMs, AI applications, agents, models, and associated data from threats such as prompt injection, agent hijacking, model poisoning, sensitive-data disclosure, insecure outputs, and unauthorized tool or API use. Growing AI adoption, limited organizational visibility into LLM usage, and concerns about shadow AI have made these platforms increasingly important, with buyers advised to look beyond vendor marketing claims and verify coverage of OWASP LLM Top 10 risks. Key selection criteria include flexible SaaS, self-hosted, or hybrid deployment; comprehensive traffic and asset visibility; straightforward integration with security, identity, cloud, and software delivery tools; demonstrable detection and prevention performance; DevSecOps support for testing AI systems before deployment; and governance capabilities such as policy enforcement, audit trails, and compliance reporting. Organizations should also assess supported models and frameworks, event investigation workflows, threat-research investment, product roadmaps, data-use practices, licensing, scalability, support, and contract flexibility. Cross-functional involvement from application development, procurement, compliance, risk, and legal teams can help ensure that the chosen platform fits both current technical needs and long-term AI strategy.

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