SPLX, a Zscaler Company

Name: SPLX, a Zscaler Company
Brand: SPLX, a Zscaler Company
Rating: 5 (1 reviews)

AI Native

The end-to-end platform to test, protect, and govern AI at enterprise scale

Est 2023|Valuation $40.6M|Raised $7M|5 Ref Customers

LinkedIn 7K|Web traffic 8K|FTEs 21|HQ New York, New York, US

01Competitive Landscape

| Aspect | SPLX | Protect AI | Mindgard | |--------|------|------------|----------| | Starting Price | Public marketplace listing for a red-teaming module with a trial and published tiered professional/enterprise pricing available from the vendor (trial plus published professional and enterprise tiers) | Public pricing not standardized; third-party sources indicate variable pricing and should be validated directly with the vendor | Sales-led; pricing not publicly posted (contact required) | | Suitable For | Enterprises seeking a combined approach to asset discovery, automated red teaming, runtime guardrails, and governance, and organizations evaluating agent workflow visibility | Teams seeking broader ML/AI security coverage across model scanning, supply chain, red teaming, and runtime controls | Organizations focused on robustness, threat modeling, red teaming, and monitoring with emphasis on technical analysis | | Key Strength | Emphasis on continuous automated red teaming and agent workflow scanning, plus open-source workflow tooling to aid discovery and adoption; now part of a larger acquirer which can affect distribution and integration pathways | Broad platform positioning across multiple AI/ML security layers per vendor/secondary descriptions | Earlier entrant focusing on robustness and monitoring features (positioning reported in comparison sources) | | Key Weakness | Limited independent review volume publicly available; limited publicly documented enterprise IAM and data residency detail; post-acquisition integration risks to validate | Pricing and packaging complexity reported by third-party sources; commercial terms vary | Public documentation and independent review coverage may be limited; requires proof of efficacy in POCs |

Narrative analysis

Direct competitors and key differentiators
- SPLX emphasizes automated red teaming, AI asset management, runtime inspection, governance mapping, and a public workflow scanner to accelerate discovery and documentation of agent tool usage.
- Other vendors emphasize broader ML supply-chain security, different runtime enforcement models, or more mature review footprints; buyers should evaluate specific requirements such as model artifact scanning and pipeline governance when comparing vendors.
Feature comparison: where SPLX may excel vs lag behind
- Possible SPLX advantages
  - Public workflow scanner for agentic workflows that can assist with discovery and documentation.
  - Integration into a larger vendor stack after acquisition may provide additional distribution and platform integration opportunities, which should be validated.
- Possible SPLX disadvantages
  - Limited independent review volume and a need to validate depth in ML pipeline and supply-chain security capabilities.
Pricing comparison: value proposition vs alternatives
- SPLX has public pricing references via a marketplace listing for its red-teaming module; many competitors are sales-led with limited public pricing, requiring side-by-side POCs and negotiated enterprise bundles.
Customer testimonials comparing SPLX to competitors
- No credible third-party narratives were identified that directly compare SPLX and competitors in a way that attributes selection reasons to one vendor over another in publicly accessible sources.
Market share and adoption trends
- There is insufficient public data to quantify SPLX market share in AI security.
- The acquiring vendor’s public positioning suggests SPLX is a strategic component of the acquirer’s AI security roadmap, but buyers should validate product roadmap implications.
When customers choose competitors instead (and why)
- Buyers primarily focused on model governance and ML supply chain security may prefer vendors with deeper ML-native controls.
- Buyers that can rely on cloud-provider guardrails for runtime content safety may consolidate on cloud-native controls rather than add a dedicated third-party vendor.

01Competitive Landscape

Aspect	SPLX	Protect AI	Mindgard
Starting Price	Public marketplace listing for a red-teaming module with a trial and published tiered professional/enterprise pricing available from the vendor (trial plus published professional and enterprise tiers)	Public pricing not standardized; third-party sources indicate variable pricing and should be validated directly with the vendor	Sales-led; pricing not publicly posted (contact required)
Suitable For	Enterprises seeking a combined approach to asset discovery, automated red teaming, runtime guardrails, and governance, and organizations evaluating agent workflow visibility	Teams seeking broader ML/AI security coverage across model scanning, supply chain, red teaming, and runtime controls	Organizations focused on robustness, threat modeling, red teaming, and monitoring with emphasis on technical analysis
Key Strength	Emphasis on continuous automated red teaming and agent workflow scanning, plus open-source workflow tooling to aid discovery and adoption; now part of a larger acquirer which can affect distribution and integration pathways	Broad platform positioning across multiple AI/ML security layers per vendor/secondary descriptions	Earlier entrant focusing on robustness and monitoring features (positioning reported in comparison sources)
Key Weakness	Limited independent review volume publicly available; limited publicly documented enterprise IAM and data residency detail; post-acquisition integration risks to validate	Pricing and packaging complexity reported by third-party sources; commercial terms vary	Public documentation and independent review coverage may be limited; requires proof of efficacy in POCs

Narrative analysis

Direct competitors and key differentiators
- SPLX emphasizes automated red teaming, AI asset management, runtime inspection, governance mapping, and a public workflow scanner to accelerate discovery and documentation of agent tool usage.
- Other vendors emphasize broader ML supply-chain security, different runtime enforcement models, or more mature review footprints; buyers should evaluate specific requirements such as model artifact scanning and pipeline governance when comparing vendors.
Feature comparison: where SPLX may excel vs lag behind
- Possible SPLX advantages
  - Public workflow scanner for agentic workflows that can assist with discovery and documentation.
  - Integration into a larger vendor stack after acquisition may provide additional distribution and platform integration opportunities, which should be validated.
- Possible SPLX disadvantages
  - Limited independent review volume and a need to validate depth in ML pipeline and supply-chain security capabilities.
Pricing comparison: value proposition vs alternatives
- SPLX has public pricing references via a marketplace listing for its red-teaming module; many competitors are sales-led with limited public pricing, requiring side-by-side POCs and negotiated enterprise bundles.
Customer testimonials comparing SPLX to competitors
- No credible third-party narratives were identified that directly compare SPLX and competitors in a way that attributes selection reasons to one vendor over another in publicly accessible sources.
Market share and adoption trends
- There is insufficient public data to quantify SPLX market share in AI security.
- The acquiring vendor’s public positioning suggests SPLX is a strategic component of the acquirer’s AI security roadmap, but buyers should validate product roadmap implications.
When customers choose competitors instead (and why)
- Buyers primarily focused on model governance and ML supply chain security may prefer vendors with deeper ML-native controls.
- Buyers that can rely on cloud-provider guardrails for runtime content safety may consolidate on cloud-native controls rather than add a dedicated third-party vendor.

SPLX, a Zscaler Company

01Competitive Landscape

Narrative analysis

02Market Map

01Competitive Landscape

Narrative analysis

02Market Map