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

What is an AI security solution?

For this blog we’ll define AI security solutions as  those that protect your LLMs and AI applications from threats that are unique to AI like prompt injection, agent goal hijacking, and others. It’s also common for some vendors to use the term to define how AI is used within security solutions to make them more effective, for example using LLM models to help customers investigate incidents more quickly. This blog will focus on the first definition, protecting AI. . 61% of new applications are now AI-powered, and most organizations cannot see where AI runs (State of AI-Native Application Security 2025). As a result, getting visibility into AI within your environment and then protecting it has become a key focus of security teams. 

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According to Gartner, 48% of leaders at organizations with high AI maturity identify security threats as one of their top three barriers to AI implementation. This makes implementing an AI security solution (and selecting the right one) a critical move for companies today.

As organizations deploy LLMs, AI copilots, and autonomous agents across business workflows, the market for AI security solutions has expanded just as quickly. This guide explains how to evaluate AI security solutions, compare them by use case, and identify the buying criteria that matter before making a purchasing decision.

What is an AI security solution, and how does it differ from AI cybersecurity solutions?

AI security solutions encompass technologies designed to protect AI systems such as AI applications, LLMs, models, and the data they process against emerging risks and threats that are unique to AI such as:

• prompt injection
• agent goal hijacking
• model/data poisoning
• model abuse
• calls to unauthorized APIs, tools, MCPs, etc.

AI-specific risks continue to evolve as organizations deploy LLMs across business workflows. The OWASP Top 10 for LLM Applications Project highlights prompt injection, sensitive information disclosure, and insecure output handling among the most significant risks organizations should address.

Unfortunately, with AI being such a hot market right now, every cybersecurity vendor claims to help protect AI in one way or another.  It’s important to get beyond marketing claims into real use cases and proof points to identify the enterprise AI security solution(s) that align with your environment today while supporting future AI initiatives. As a baseline, the solution should be able to detect and block AI-specific attacks covered in the OWASP LLM Top 10, as well as protect agents, MCPs, and other AI assets.

What are the key buying criteria for AI security solutions?

Understanding what features you need  is only the first step. The next challenge is determining whether the solution fits your organization's AI strategy, security architecture, and compliance requirements. While many vendors promise similar AI capabilities, several otherfactors consistently separate mature platforms from products built around marketing claims.

• Deployment and data collection options. Choose a platform that aligns with your security and regulatory requirements, whether that is SaaS for faster adoption or self-hosted and hybrid deployments for greater control over sensitive data. Also, how does the vendor collect data? To have complete visibility, the vendor needs to be able to collect data out-of-band, inline, and at the edge. It also needs to see north/south traffic and east/west traffic.
• Integration simplicity. The platform should integrate with your existing security operations, identity and access management (IAM), software delivery, and cloud security tools to provide unified visibility and reduce operational silos. And the integration should be easy, not require significant implementation resources.
• Provable detection efficacy. Anyone can claim to find vulnerabilities, detect attacks, block unwanted interactions. But how well they do those things is the real issue. Look for details on detections, proof points, and where possible do a proof of concept/proof of value before purchasing.
• Integration with development workflows. Sophisticated solutions can integrate with DevSecOps to “shift left,” test AI apps and agents, then feed that analysis back into the developer workflow to improve security posture continuously.
• Compliance and governance enforcement. Look for features such as policy enforcement, audit trails, and reporting that support compliance with frameworks like SOC 2, ISO/IEC 27001, and the NIST AI Risk Management Framework (AI RMF). As organizations move AI into production, governance has become a core evaluation criterion, not just for compliance but for managing AI-related risks throughout the AI lifecycle. The NIST AI RMF provides practical guidance for governing, measuring, and managing those risks.

What technical questions should you ask?

Technical capabilities often determine whether a platform can support your organization's current AI environment and adapt as it evolves. During product evaluations, consider asking:

• Do you protect against all of the OWASP LLM Top 10? 
• Beyond OWASP, what other attacks and abuse do you protect against?
• Which deployment models (SaaS, self-hosted, or hybrid) do you support?
• Which AI models, frameworks, and deployment environments are supported?
• How does the platform discover, monitor, and protect AI applications, models, and other AI assets?
• Which security operations, identity, and software delivery tools integrate natively with the platform?
• What other security tool integrations do you offer (SIEM, WAF, etc.)
• How are security events logged, investigated, and audited?
• How do you update detections and decide what new features to add?
• How much of your budget is invested in threat research?
• Can you walk me through your roadmap for the next 12 months?

What commercial and contract factors should you consider?

A technically capable platform is not always enough to justify a long-term investment. Procurement teams should also evaluate how well a solution aligns with the organization's operational and commercial requirements.

Consider how the vendor uses your data, trains its security solution, the licensing model, implementation effort, scalability, and customer support. Ask whether the vendor provides:

• An AI survey on how it uses AI, plus how it uses (or doesn’t use) your data in model training
• compliance documentation
• customer references, and
• a clear product roadmap for addressing emerging AI security risks

Finally, review contract flexibility, including expansion terms, and exit provisions, to ensure the platform can evolve alongside your AI strategy.

Remember to include input from other teams in your organization

It’s also useful to remind yourself that it’s possible to pick the ideal solution and still have it be suboptimal for your organization. That usually happens when a decision is made in isolation. Consider bringing in other teams, including:

• Application development: This team likely is building many of the AI assets you need to protect, and particularly if you are trying to find vulnerabilities before deployment, collaborating with this team can help you find a vendor that’s the best overall fit
• Procurement: they will be the negotiation experts and can lead the commercial part of the deal
• Compliance/risk: this team likely already has a security and AI usage survey that you can give to the vendor to check on issues like how they use your data
• Legal: they will check any issues with either the contract terms, although in many organizations legal will be brought in by procurement once the RFP process or negotiation begins

Selecting a cybersecurity solution requires significant due diligence, and AI-specific solutions need even more than normal. Find the right solution by looking at functionality/fit-to-purpose, architectural fit, commercial terms, and broader fit with development workflows.