When does building AI / LLM Security make sense?

Building is credible for the runtime guardrail layer, where NeMo Guardrails, garak, and Lasso's open-source version are production-viable and free. Teams with specific AI deployments that would require heavy configuration of vendor tools anyway often find an OSS policy engine easier to maintain.

When does buying AI / LLM Security make sense?

Buying makes sense when AI asset discovery across the organization is the primary need — identifying unsanctioned AI tools, shadow LLM usage, and AI components in SaaS applications. That AI-SPM layer is harder to self-build and is where commercial vendors are meaningfully ahead of open-source options.

What are the main AI / LLM Security vendors?

Representative vendors include Lakera Guard (Check Point), Aim Security, Prisma AIRS (Palo Alto, incl. Protect AI), Lasso Security. B4 Pro scores the full set.

How fast is the AI security vendor landscape changing?

Very fast — this category barely existed as a defined market two years ago. Several vendors have already been acquired (Lakera by Check Point, Protect AI into Prisma AIRS). Contract flexibility and exit clauses deserve attention when buying, since consolidation and repositioning are still ongoing.

Security & Compliance · Engineering, IT & AI

Should you build or buy AI / LLM Security (Runtime Guardrails & AI-SPM)?

AI / LLM Security software addresses two related problems: runtime guardrails that filter prompt injection attacks, prevent data egress, and enforce policy on model outputs; and AI Security Posture Management (AI-SPM) that discovers which AI models and applications are running across an organization and assesses their risk exposure. It's used by security teams deploying or managing LLMs in production.

The build-vs-buy decision for AI / LLM Security turns on how much of the runtime guardrail layer is already covered by mature open-source frameworks versus where AI asset discovery and posture management require commercial investment, and how fast the vendor landscape is moving given that this category barely existed two years ago; your AI deployment footprint and discovery requirements decide it.

Domain: Security & Compliance
Function: Engineering, IT & AI
Industries: Cross-industry

Last assessed June 2026 · re-scored quarterly via The Continuum.

Build it, buy it, or bridge?

	Build it	Buy it	Bridge (buy, then extend)
Cost shape	OSS guardrails (NeMo, garak) are free; commercial ranges $50K-$5M+ for full platform	Entry-level commercial options exist; full AI-SPM platforms carry enterprise pricing	OSS for runtime guardrails; vendor for AI asset discovery and posture management
Time to value	OSS guardrails deployable in days; custom policy engines take longer	Vendor guardrails live in days to weeks; AI-SPM discovery takes longer to configure	OSS for immediate guardrail coverage; vendor AI-SPM added for discovery scope
Differentiation captured	Guardrail logic is a cost of doing AI; the AI models themselves are the differentiator	Vendor discovery finds shadow AI assets internal tooling can't see	OSS for the enforcement layer; vendor for the inventory and posture visibility
AI feasibility today	Runtime guardrails using NeMo, garak, or Lasso MIT version are production-proven	AI-SPM discovery and posture management are harder to replicate without vendor tooling	OSS handles guardrail filtering; vendor handles cross-org AI asset discovery
Who it fits	Teams with specific AI deployments who can tune OSS policy engines to their use case	Organizations needing discovery of unsanctioned AI tools across a large workforce	Security teams wanting OSS runtime enforcement with commercial AI inventory coverage

The B4 call

B4 has a verdict for AI / LLM Security (Runtime Guardrails & AI-SPM).

Build, Buy, Bridge, or Beware, with the five-dimension scorecard and the reasoning behind it. Unlock the call, and every other category, with B4 Pro.

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When building AI / LLM Security (Runtime Guardrails & AI-SPM) makes sense

Runtime guardrails that filter prompt injection, block data egress, and enforce policy on model outputs are buildable today with open-source tooling. NeMo Guardrails, garak, and Lasso's MIT-licensed version are production-viable and free. Multiple teams run self-built AI firewalls in production using rule engines combined with LLM classification for output validation. The build case gets serious when your AI deployment is specific enough that a generic policy engine requires heavy customization regardless — if you're customizing 70% of a vendor's guardrail configuration anyway, the boundary between buying a platform and building a targeted internal tool narrows significantly. OSS guardrail frameworks also let you iterate on policy logic quickly without navigating a vendor's feature roadmap. For teams with a defined set of AI applications and a security engineer willing to maintain the policy layer, OSS coverage is often sufficient.

When buying AI / LLM Security (Runtime Guardrails & AI-SPM) makes sense

The vendor case for AI / LLM Security is strongest on the discovery side. Finding which AI models are running across your organization — which SaaS tools embed LLMs, which employees are using unsanctioned AI applications, and which internal applications have AI components touching sensitive data — is an AI-SPM problem that's harder to self-build than the runtime guardrail layer. Vendors like Lakera Guard and Aim Security are ahead of OSS options specifically on discovery and posture management. The vendor landscape in this category is moving fast enough that the right platform today may look quite different in 18 months. That's both a caution and a buy argument: early-stage platforms need scrutiny on contract flexibility, but the discovery capabilities they offer today have no direct OSS equivalent.

AI security is both a genuine new requirement and a fast-moving OSS space. Runtime guardrails that filter prompt injection, block data egress, and enforce policy on model outputs can be assembled today using NeMo Guardrails, garak, or Lasso's MIT-licensed version. Multiple teams run self-built AI firewalls in production. The build case gets serious when your AI deployment is large enough and specific enough that a generic policy engine won't fit without heavy customization anyway.

Buying earns its keep when you need AI asset discovery across the organization, beyond runtime filtering. Finding which AI models are running where, what data they're touching, and whether they're inside or outside sanctioned tooling, that AI-SPM layer is harder to self-build and newer as a product category. Vendors like Lakera Guard and Aim Security are ahead of the OSS options on discovery and posture management specifically. The vendor landscape is changing fast enough that platform bets today may look quite different in 18 months.

Representative vendors

Lakera Guard (Check Point)Prisma AIRS (Palo Alto, incl. Protect AI) and 3 more, scored in B4 Pro

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Frequently asked

What is AI / LLM Security (Runtime Guardrails & AI-SPM) software?: AI / LLM Security software addresses two related problems: runtime guardrails that filter prompt injection attacks, prevent data egress, and enforce policy on model outputs; and AI Security Posture Management (AI-SPM) that discovers which AI models and applications are running across an organization and assesses their risk exposure.
When does building AI / LLM Security make sense?: Building is credible for the runtime guardrail layer, where NeMo Guardrails, garak, and Lasso's open-source version are production-viable and free. Teams with specific AI deployments that would require heavy configuration of vendor tools anyway often find an OSS policy engine easier to maintain.
When does buying AI / LLM Security make sense?: Buying makes sense when AI asset discovery across the organization is the primary need — identifying unsanctioned AI tools, shadow LLM usage, and AI components in SaaS applications. That AI-SPM layer is harder to self-build and is where commercial vendors are meaningfully ahead of open-source options.
What are the main AI / LLM Security vendors?: Representative vendors include Lakera Guard (Check Point), Aim Security, Prisma AIRS (Palo Alto, incl. Protect AI), Lasso Security. B4 Pro scores the full set.
How fast is the AI security vendor landscape changing?: Very fast — this category barely existed as a defined market two years ago. Several vendors have already been acquired (Lakera by Check Point, Protect AI into Prisma AIRS). Contract flexibility and exit clauses deserve attention when buying, since consolidation and repositioning are still ongoing.

The B4 Index scores every software category on two axes, strategic differentiation and AI feasibility, to classify it Build, Buy, Bridge, or Beware. See the full methodology.

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