AI Security Solutions — Complete 2026 Buyer's Guide
Buyer's Guide · 2025-09-12 · 22 min read · FilterPrompt Security Team
Compare the leading AI security solutions for LLM apps in 2026. Categories, vendors, pricing, OWASP LLM Top 10 coverage, and how to pick the right stack.
AI security solutions are the controls, tooling, and processes that stop generative AI applications from leaking data, executing adversarial instructions, producing harmful output, or being abused at the model layer. This guide is the buyer-side companion to the OWASP LLM Top 10 — it maps every risk to a category of AI security solution, names the leading vendors in each category, and gives you a concrete shortlisting framework so you can ship a stack that actually matches your threat model.
What counts as an AI security solution
AI security solutions are not a single product category — they're a stack. The taxonomy that actually holds up in procurement and architecture reviews is six layers, each with its own vendor landscape, evaluation criteria, and integration surface. The mistake teams make is buying one product and assuming it covers the rest. It doesn't.
- AI firewalls — real-time prompt and response inspection (Lakera, Cloudflare Firewall for AI, FilterPrompt, Robust Intelligence)
- LLM vulnerability scanners — adversarial probe batteries that produce OWASP LLM Top 10 reports (FilterPrompt Scanner, Garak, PyRIT, Mindgard)
- AI / LLM gateways — multi-tenant routing, quotas, observability (Portkey, Kong AI Gateway, LiteLLM, FilterPrompt)
- DLP / PII redaction for prompts — pre/post filters that strip sensitive identifiers (Nightfall AI, FilterPrompt, Skyflow)
- Agent guardrails — action allowlists, tool-call inspection, sandbox runtimes (Nvidia NeMo Guardrails, Guardrails AI, FilterPrompt validators)
- AI-SOC + monitoring — verdict logs, anomaly detection, incident response (Datadog LLM Observability, Arize, Helicone, FilterPrompt audit)
Map every OWASP LLM Top 10 risk to a category
The takeaway from this matrix: no single AI security solution covers the full Top 10. The fastest path to credible coverage is one combined firewall + gateway + scanner that shares the same detection engine — so verdict semantics are consistent across realtime block decisions, scheduled scans, and audit logs.
AI security solutions market — categories explained
1. AI firewalls
An AI firewall is the same primitive a WAF is for HTTP — it inspects every prompt and response for adversarial input, sensitive data, and policy violations, and it allows / sanitizes / blocks the request before it reaches the model. The category is the entry point for most AI security solution shopping: it's where prompt injection (the #1 risk) gets stopped, and it's the easiest control to demo to a security committee. Leading vendors: Lakera Guard, Cloudflare Firewall for AI, FilterPrompt, Protect AI. Differentiators: detection accuracy on per-attack-family splits, false positive rate on benign traffic, latency budget, multi-provider support, audit log depth.
2. LLM vulnerability scanners
Where a firewall blocks attacks in real time, a scanner finds vulnerabilities on a schedule. It runs adversarial probe batteries (jailbreaks, prompt injection, PII extraction, harmful content, tool-call injection) against a target LLM and produces an OWASP LLM Top 10 vulnerability report with per-probe evidence and a prioritized fix list. Scanners are the audit-ready artifact security committees ask for. Leading tools: FilterPrompt Scanner, Garak (open source), PyRIT (Microsoft, open source), Mindgard. Differentiators: probe library size, evaluator quality (multi-stage detection), repeatability of results, and integration with the firewall layer.
3. AI / LLM gateways
An AI gateway is the routing and observability layer in front of multiple LLM providers. It handles per-tenant rate limits, monthly quotas, model allowlists, IP rules, cost attribution, and provider failover. Gateways are not security tools by themselves — but a gateway with a firewall stapled in front of it is the standard production architecture for multi-tenant LLM apps. Leading vendors: Portkey, Kong AI Gateway, LiteLLM (open source), FilterPrompt (gateway + firewall combined). Differentiators: bring-your-own-key support, per-tenant isolation guarantees, observability surface, OpenAI-compatibility for drop-in adoption.
4. DLP / PII redaction for prompts
PII / DLP layers strip sensitive identifiers — emails, phones, SSNs, passport numbers, tax IDs, credit cards, secrets, internal IDs — from both prompts (before they reach the model and the provider's logs) and responses (before they reach the user). The 'prompts' part is what makes this an AI security category and not a generic DLP one: traditional DLP tools don't operate on conversational text or handle the post-filter case. Leading vendors: Nightfall AI, Skyflow, FilterPrompt PII layer.
5. Agent guardrails
Agent products run tool calls, hit external APIs, and execute multi-step plans. The risk surface is enormous: prompt injection delivered through a tool result can trigger destructive operations under the user's identity. Guardrails enforce action allowlists, validate tool-call shapes, sandbox code execution, and gate destructive actions behind explicit policy. Leading vendors: Nvidia NeMo Guardrails, Guardrails AI (open source), FilterPrompt validators, OpenAI's built-in tool-permission system.
6. AI-SOC + monitoring
Once the proxy is in place and probes are running on a schedule, the next layer is observability: verdict logs, anomaly detection on prompt patterns, incident response playbooks, and SIEM integration. The right way to think about this is the AI version of a SOC stack — your firewall produces verdicts, your monitoring tool aggregates them, and your incident process triages them. Leading vendors: Datadog LLM Observability, Arize, Helicone, FilterPrompt audit.
How to evaluate AI security solutions
A proper evaluation rubric for any AI security solution has 7 axes. Score each vendor on every axis and the right pick falls out. Skipping the rubric is how teams end up with shelf-ware that looked great in a demo and didn't survive contact with production traffic.
- Threat coverage — which OWASP LLM Top 10 risks does it actually mitigate, with evidence?
- Detection accuracy — per-attack-family precision/recall, not a single aggregate number
- False positive rate — measured on a real benign control set, not synthetic benign prompts
- Latency budget — median and p95 firewall overhead; anything over 200ms median will get bypassed
- Provider coverage — OpenAI / Anthropic / Gemini / Azure / Bedrock / open-source / custom
- Multi-tenancy + auditability — per-tenant isolation, verdict logs, replay, SIEM export
- Integration cost — drop-in OpenAI-compatible proxy is 1 day of work; custom SDK is 1–3 weeks
Pricing models you'll see
Build vs buy — when each makes sense
Building your own AI security solution is tempting because the primitives look simple — regex rules, an embedding model, a JSON schema validator. The reality at production scale is the maintenance burden of the rule corpus, the eval harness for measuring detection accuracy as the rules and models drift, the per-attack-family benchmarking discipline, the audit log retention story, and the multi-tenant isolation guarantees. Most teams that try to build run for 6–12 months and then buy. The cases where build genuinely wins: a single-model deployment with a hostile threat model and a security team large enough to staff continuous red teaming, or an air-gapped deployment where no third-party proxy is allowed. Everywhere else, buy.
FilterPrompt as an AI security solution
FilterPrompt is an AI firewall, vulnerability scanner, and multi-tenant gateway in one product. The same detection engine powers all three, which is what keeps verdict semantics consistent across realtime block decisions, scheduled scans, and audit logs. It's provider-agnostic (OpenAI, Anthropic, Gemini, Azure, Bedrock, OpenRouter, any OpenAI-compatible endpoint), tenants bring their own provider keys, and the proxy is OpenAI-compatible so adoption is one base-URL change in your existing SDK.
Sample procurement shortlist
If you're building the shortlist this quarter, the smallest credible bake-off is one vendor per category with 30 minutes of probe traffic from your real product on each. The questions to ask in every demo:
- Show me the per-attack-family precision/recall on your published benchmark
- Show me the false positive rate on a benign control set you didn't author
- What is median and p95 firewall latency at production thresholds?
- How are tenants isolated — at the rule layer, the credential layer, the log layer?
- What does the verdict log look like? Can I export it to Splunk / Datadog / a SIEM?
- How do I run a scheduled scan and get a vulnerability report I can hand to an auditor?
- What happens when a new attack family appears in the wild — what's your update cadence?