🛒 AI Security Buyer's Guide: How to Choose

How to choose an AI security solution is a task with no single "right" answer: the market for protecting LLM applications is young, the terms (AI Firewall, guardrails, LLM gateway, AI-SOC) overlap, and vendor approaches differ in deployment model, depth of detection, and honesty of claims. This buyer's guide offers a neutral set of criteria, a map of solution categories, and a list of questions worth asking any vendor — so you choose AI defense for your threat model, not for someone else's marketing. At the end: how the Spectorn platform fits these criteria (two regional cloud contours, Rust engines, a hash-linked audit chain).

Before reading, the conceptual pages are useful: What Is an AI Firewall and LLM Guardrails: How They Differ from a Firewall. For rollout practice, see the checklist for securing an LLM in production.

Why a Dedicated AI Security Solution at All

LLM applications opened up a class of threats that classic software does not have: prompt injection, token theft via agents, PII leakage through RAG, the "lethal trifecta," knowledge-base poisoning. A network firewall and WAF do not see them — they inspect packets, while LLM attacks look like valid text. That is why a dedicated category of AI defenses emerged. The industry reference for risks is the OWASP Top 10 for LLM (2025); it is convenient for building requirements for the solution you are buying.

Solution Categories: How Not to Confuse the Terms

Before comparing vendors, understand which category each of them plays in — "AI security" claims hide very different things:

Scroll horizontally to see all columns when needed.

Category	What it does	When it fits
AI Firewall / LLM Firewall	Semantic inspection of prompts, responses, and actions; a single control point + threat database	An adversarial threat model: injection, data theft, agents
Guardrails	Control of model behavior (topic, tone, format, basic filtering)	Product quality and behavior compliance
Inference scanners	Libraries of input/output scanners (PII, injection, secrets) embedded in code	A fast start, control at the application level
AI gateway	A proxy to models + quotas, keys, routing (+ optional security)	Managing access and model spend
AI-SOC	Event correlation, incidents, playbooks, audit	Operational security, investigations, the regulator

Mature defense often combines several layers. A detailed breakdown of the "firewall vs guardrails" pair is in the dedicated guide.

The Landscape of Approaches (Neutral, by the Facts)

To orient yourself, it helps to know public reference points for the categories (without "better/worse" judgments):

API-first SaaS (for example, Lakera Guard) — single-API inspection of input and output as a service; a fast path to production, but traffic goes to an external API.
Open-source guardrails (for example, NVIDIA NeMo Guardrails) — programmable rails and dialog control in Colang; self-hosted, but attack detection is often reinforced with third-party classifiers.
Open-source inference scanners (for example, Protect AI / LLM Guard) — sets of input/output scanners (PII, injection, secrets) under a permissive license, embedded in the application code.

This is not a ranking or a head-to-head comparison: each approach has its own niche. Your choice is determined by the threat model, data requirements (whether traffic may leave the perimeter), and whether you need SOC-grade correlation and audit.

Selection Criteria: The Buyer's Checklist

1. Deployment Model and Data Residency

The main watershed. Regulated industries often require that data not leave the perimeter.

☑️ Is there a self-hosted / on-prem deployment in which traffic does not go outside?
☑️ Are isolated (air-gapped) networks supported?
☑️ Where do data and logs physically live; is there regional hosting?
☑️ If SaaS — what data goes to the vendor and how is it stored?

Spectorn: Spectorn runs as a cloud service in two separate contours, RU and Global, and traffic stays in its region's contour; installation inside the customer's perimeter is not part of the current release.

2. Depth and Honesty of Detection

☑️ Does the solution cover the core of the OWASP LLM Top 10: injection, data leakage, insecure output, excessive agency?
☑️ Semantic injection detection or just a list of forbidden words?
☑️ Is there agent-action control (the "lethal trifecta," tool abuse)?
☑️ Honesty of claims: does the vendor state openly what the solution does not cover (supply chain, rate-limiting, secure coding)? Beware of "best-in-class" without basis and invented accuracy percentages.

3. Audit and Correlation (Regulator Readiness)

☑️ Is there a tamper-evident log of decisions (a hash chain usable as evidence)?
☑️ Are the requestor's identity, tool-call details, result, and latency recorded?
☑️ Is there event correlation into an incident with a mapping to OWASP/MITRE ATLAS?
☑️ SIEM/SOC integration?

Spectorn: each verdict goes into a hash-linked SHA-256 chain without request content, and every finding reaches your SIEM as a JSON event over HTTPS (spectorn.ai-finding.v1). Correlation into incidents is built in your SIEM; Spectorn's own SOC correlation (GoMCP) is not deployed in the RU and Global contours.

4. Performance and Latency

☑️ What is the inspection latency on your traffic (not someone else's benchmark)?
☑️ Is a "cheap checks first" cascade used against extra latency?
☑️ Is a GPU required, or is CPU sufficient?

Do not take someone else's numbers on faith — measure latency in a pilot on your own load profile.

5. Compliance and Certification

☑️ Conformance to applicable frameworks: GDPR/FZ-152, the EU AI Act, NIST AI RMF, industry ones (HIPAA, PCI DSS, FSTEC).
☑️ Support for DPIA for high-risk processing.
☑️ Transparency of the certification roadmap.

6. Integration and Operations

☑️ How does the solution slot into your stack: proxy, SDK, sidecar, gateway?
☑️ Policy configurability for the scenario?
☑️ Is it vendor-agnostic across models and providers?
☑️ Budget for the operational overhead of self-hosting (a practical reference point is on the order of 15–20% of the initial effort annually for maintenance).

7. Pricing Transparency

☑️ A clear pricing model (per request/token/instance) without hidden thresholds.
☑️ Predictable spend as load grows.

How Spectorn Fits the Criteria

The Spectorn defense layer is a set of engines for detecting and blocking attacks on LLMs that runs as part of the platform. Spectorn runs as a cloud service in two separate contours, RU and Global, and traffic stays in its region's contour; installation inside the customer's perimeter is not part of the current release. The positioning is honest and verifiable against the criteria above:

Deployment / residency — Spectorn runs as a cloud service in two separate contours, RU and Global, and traffic stays in its region's contour; installation inside the customer's perimeter is not part of the current release. Engines in Rust, CPU-efficient inspection without a mandatory GPU.
Detection depth — the engines cover the core of the OWASP LLM Top 10: injection, jailbreak, pii, exfiltration, output_scanner plus agent control lethal_trifecta, tool_abuse, cross_tool_guard, goal_predictability, intent_revelation, model_containment, dormant_payload, social, resource_exhaustion.
Audit / correlation A pattern spread over many events is a job for your SIEM: Spectorn sends each finding there with the rule, category, severity and verdict_id; its own SOC correlation (GoMCP) is not deployed in the cloud contours.
Honest boundaries — Spectorn states openly that it does not replace supply-chain management (SBOM/ML-BOM), gateway rate-limiting, or secure coding of tools; it complements them with detection, correlation, and audit. No invented benchmarks and no "best-in-class" without basis.

🛠️ Policy settings

There is no configuration file to deploy: the platform operator sets your tenant's policy at your request, including the scan mode (enforce, or shadow to see what would be blocked without blocking it), allow and deny lists, the block threshold and category weights. The engines that matter most for this sector: injection, jailbreak, output_scanner, pii, exfiltration, lethal_trifecta, goal_predictability. Start in shadow mode on your own traffic, review what would have been blocked, then enforce. Masking of personal data is not enabled in the cloud contours: a request with personal data is blocked or flagged, not rewritten.

Pilot tip: run a set of real and attacking prompts through the profile, measure latency on your own traffic, and verify that the Decision Logger produces an audit-grade trail. This gives an objective basis for the decision, not someone else's numbers.

❓ FAQ

How do I choose an AI security solution?

Start with a threat model based on the OWASP LLM Top 10, then evaluate vendors against seven criteria: deployment model and data residency, depth and honesty of detection, audit and correlation, latency, compliance, integration, and pricing transparency. For your threat model, not for marketing.

How does an AI Firewall differ from guardrails when choosing?

Guardrails control the model's behavior (topic, tone, format); an AI Firewall is a security layer with attack detection, correlation, and audit. If your threat model includes a motivated attacker, guardrails alone are not enough. See the dedicated guide.

Self-hosted or SaaS — what to choose for AI security?

It depends on data requirements. If data must not leave the perimeter (regulated industries, state secrets, healthcare), you need self-hosted/on-prem. SaaS gives a faster start, but traffic goes to an external API — assess what data leaves the perimeter in that case.

What should I look for in the honesty of a vendor's claims?

A mature vendor states openly what the solution does not cover (supply chain, rate-limiting, secure coding of tools). Beware of "best-in-class" without basis, invented accuracy percentages, and promises to "cover all 10 OWASP categories" — some of them are solved organizationally.

Which criteria matter for regulated industries?

Data residency (traffic within the perimeter), tamper-evident audit (a hash chain as evidence), conformance to applicable frameworks (the EU AI Act, NIST AI RMF, GDPR/FZ-152, industry ones), support for isolated networks, and DPIA for high-risk processing.

How do I compare the latency of different solutions?

Not by someone else's benchmarks. Run a pilot on your traffic and measure the real inspection latency at your own load profile. Pay attention to the cascade architecture ("cheap checks first") and whether a GPU is required.

Do I need an AI-SOC on top of detection?

If investigations and presentation to a regulator matter — yes. Correlation of events into an incident (with a mapping to OWASP/MITRE ATLAS), response playbooks, and a tamper-evident decision log are what distinguish operational security from a standalone detector.

How many layers of defense do I actually need?

In production, behavioral guardrails (UX and policy) and a security layer — a firewall with detection, correlation, and audit — usually coexist. The exact mix is determined by the threat model: do not "gold-plate" defenses where there is no risk, and use defense in depth where there is a motivated attacker.