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.
Build it, buy it, or bridge?
When building 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 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.
The desk read
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.
Vendors in AI / LLM Security (Runtime Guardrails & AI-SPM)
Each file covers what the product is, its funding history, and when the index last verified it alive.
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.