AI security has become a crowded market, with products for discovering AI use, assessing AI posture, filtering prompts and outputs, testing models, securing agents and governing AI risk. The labels overlap: AI security posture management (AI-SPM), AI firewalls and guardrails, AI gateways, AI red teaming, AI governance and agent security. Many products cover two or three of these. This vendor-neutral buyer's guide explains the main categories, what each can and cannot do, how identity fits in, and how to evaluate products against your real AI estate.
Key takeaways
- Start with an inventory of how AI is used: sanctioned applications, shadow AI, AI development, agents and third-party AI features. Your needs follow from that.
- Runtime guardrails reduce prompt injection and data leakage but cannot guarantee prevention. Pair them with identity and access controls that limit what a manipulated AI can do.
- Look for products that understand identities and credentials used by AI: API keys, service accounts, OAuth grants and agent identities.
- Test with your own applications and attack scenarios, and measure latency and false positives.
Categories
| Category | What it does | Limits |
|---|---|---|
| AI discovery and usage governance | Finds AI apps, browser use, OAuth-connected AI tools and AI API keys; enforces usage policy | Visibility into custom-built AI varies |
| AI security posture management (AI-SPM) | Inventories AI models, pipelines, data and services in cloud; finds misconfigurations and risky access | Mostly cloud-focused; limited runtime protection |
| Runtime guardrails / AI firewalls | Inspect prompts, retrieved content and outputs for injection, sensitive data and policy violations | Probabilistic detection; latency; can be bypassed |
| AI gateways | Centralise model access, authentication, rate limits, logging and routing | Only cover traffic routed through them |
| AI red teaming and testing | Test models and applications for jailbreaks, injection and unsafe behaviour | Point-in-time unless continuous |
| Agent security | Agent inventory, identity, tool and MCP control, runtime monitoring. See the AI Agent Identity Security Buyer's Guide | Fast-moving; coverage of agent platforms varies |
| AI governance and risk | Model inventory, risk assessments, policy and regulatory mapping | Process-focused; limited technical enforcement |
Where identity fits
Every AI system uses identities: API keys to model providers, service accounts for pipelines, OAuth grants for AI apps, and agent identities with tool access. These determine the blast radius when AI is misused. Evaluate whether products:
- Discover AI-related credentials and their permissions. See the LLM API Key Security Guide.
- Attribute AI activity to users, applications and agents.
- Integrate with your IdP, secrets manager and NHI tooling.
- Enforce least privilege for agent tools rather than only filtering text.
Evaluation criteria
- Coverage: which AI providers, clouds, frameworks, SaaS AI features and agent platforms are supported?
- Detection quality: prompt injection, sensitive data and jailbreak detection rates on your test set; false positive rate.
- Performance: added latency and throughput under load.
- Deployment: inline proxy, SDK, API, browser extension or agentless; fit with your architecture.
- Data handling: where prompts and outputs are processed and stored; retention; whether data trains vendor models.
- Explainability: clear reasons for blocks and findings.
- Framework mapping: OWASP LLM and Agentic Top 10, NIST AI RMF, EU AI Act.
Questions to ask vendors
- Show detection of an indirect prompt injection in a retrieved document for our RAG application.
- How do you find AI API keys and OAuth-connected AI apps across our estate?
- What happens when your detection misses an injection? What other controls do you provide?
- How do you secure agent tool calls and MCP servers?
- What latency do you add, and what happens if your service is unavailable?
- Where do our prompts and outputs go, and for how long?
Red flags
- Claims to "prevent all prompt injection".
- No answer on data handling of prompts and outputs.
- No integration with identity or secrets tooling for AI credentials.
- Coverage limited to one model provider.
Proof of concept
- Choose two or three real AI use cases: an internal assistant with RAG, a customer-facing chatbot, an agent with tools.
- Prepare a test set of injection, jailbreak and sensitive data cases, plus normal traffic.
- Measure detection, false positives, latency and operational effort.
- Test discovery of shadow AI and AI credentials.
How NHI Mgmt Group can help
We provide independent AI security and agentic AI identity advisory and evaluation support. Browse vendors in our products directory or contact us.
Related NHI Mgmt Group resources: Agentic AI Security Guide · AI Agent Identity Security Buyer's Guide · Shadow AI Discovery Guide · Permission-Aware RAG Guide
