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Comparisons & Alternatives

137 posts on comparisons & alternatives.

AI Security Buying Guide: How to Evaluate Vendors Against the 2026 Compliance Stack

The AI security vendor landscape in 2026 splits across model-side guardrails, browser extensions, CASB integrations, ML observability, and identity-aware proxies. Each category solves a different problem and produces different evidence. This buying guide walks through the ten questions a CISO or compliance lead should ask any AI security vendor before purchase. The questions reflect the EU AI Act, NIST AI RMF, ISO 42001, and sector frameworks the buyer is buying against. The aim is an architectural fit decision, not a feature-checklist comparison.

ai-securitybuying-guidevendor-evaluationcomplianceeu-ai-actprocurement
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The AI Vendor Security Questionnaire: 38 Questions Procurement Should Actually Ask

Most AI vendor security questionnaires are SOC 2 templates with two AI questions tacked on. The result is a procurement process that surfaces well-formatted SOC 2 reports while leaving the AI-layer risks unmapped. This article walks through 38 questions that surface what the vendor actually does at the AI request boundary: model coverage, identity context, per-decision audit, policy enforcement, prompt-injection handling, data residency, regulatory alignment, and incident response. The questions assume the vendor is supplying an AI-using service, not a model. Each question includes the answer pattern a defensible vendor produces and the answer pattern that should trigger a deeper review.

vendor-managementprocurementai-governancesecurity-questionnairethird-party-risk
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Cisco-Astrix and the Rise of Identity-Aware AI Gateways

On May 4, 2026, SecurityWeek reported that Cisco moved to acquire Astrix Security for roughly $400M. The deal validates identity-aware AI traffic enforcement as a buying-center category. Non-human identities (NHIs) — API keys, OAuth tokens, agent identities — are the new entry point. This article walks through what the deal signals for the AI security stack, how NHI-platform-bolted-on approaches differ from inline policy enforcement at the LLM request boundary, and the RFP questions a CISO should ask before defaulting to a bundled offering.

ai-securityidentity-and-authorizationpolicy-enforcementai-governancearchitecturecybersecurity
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DeepInspect vs Aim Security: where the enforcement boundary sits

DeepInspect intercepts HTTP AI traffic between authenticated users or agents and any LLM, enforces identity-bound policy at the request layer, and writes a per-decision audit log. Aim Security sits primarily in the browser and DLP layer. This comparison walks through where each tool can and cannot enforce, what the audit trail looks like, and which one a deployer chasing the EU AI Act August 2 deadline should pick.

comparisonai-gatewayai-securityaim-securityenforcementeu-ai-act
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DeepInspect vs Credal: gateway architecture versus internal-AI portal

DeepInspect is a stateless policy gateway in front of any LLM. Credal is an internal AI assistant portal with controls bolted in around the portal product. The two get categorized together, but the enforcement boundary and the audit artifact are structurally different. This comparison walks through where each tool fits, what the per-decision log looks like, and which deployer profile each one serves.

comparisoncredalai-gatewayai-securityenforcementaudit
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DeepInspect vs Protect AI Guardian: per-decision audit versus model-scanning

Protect AI Guardian (now under Palo Alto Networks after the August 2025 acquisition) focuses on model artifact scanning and ML supply chain risks. DeepInspect operates as a stateless policy gateway in the HTTP path between authenticated users or agents and any LLM. The two product categories often get evaluated together, but the enforcement boundary, the audit artifact, and the regulatory fit are different. This piece walks through where each sits.

comparisonprotect-aipalo-altoai-gatewayenforcementml-supply-chain
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AWS Bedrock Guardrails alternatives: where the model-bound control falls short

AWS Bedrock Guardrails covers content filtering, denied topics, and PII redaction for traffic that lands on Bedrock. The control is bound to Bedrock-mediated requests. Enterprises running multi-model AI need a gateway that covers OpenAI, Anthropic direct, Azure AI, and self-hosted models with a single policy plane. This is the alternatives comparison: what the gap is, who fills it, and what to look for when evaluating.

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Credal alternatives: where the portal pattern stops working

Credal gives employees a sanctioned internal AI portal. The pattern works when employee AI usage is the entire scope. The pattern stops working when machine-to-machine, agent-driven, or vendor-embedded AI traffic must be covered by the same policy and the same audit trail. This piece walks through where the portal stops and what fills the gap.

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When Outbound AI Touches Customer Data: Security Context for Lemlist-Style Sales AI Stacks

Sales outreach platforms like Lemlist, Outreach, Apollo, and Smartlead now embed AI features that consume CRM and customer data to draft messages and personalize sequences. The security question is not which platform has the cleanest UI. It is where the AI traffic exits the enterprise boundary, what data leaves with it, and who holds the audit record. The architectural answer is upstream of the platform choice.

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IBM AI Governance: Where watsonx.governance Fits and Where Independent Enforcement Still Matters

IBM watsonx.governance is the model lifecycle governance product from IBM, covering model risk management, model documentation, agent evaluation, and production monitoring. IBM also ships a model gateway in watsonx.ai that routes inference across OpenAI, Anthropic, Bedrock and others. This article walks through what each of those covers, where the boundary ends, how the April 2026 SR 26-2 model risk guidance changes the banking picture, and what an independent enforcement layer at the AI request boundary adds.

ai-governanceibmmodel-risk-managementai-control-planemlops
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Collibra AI Governance: Where the Data Intelligence Approach Ends and Request-Level Enforcement Begins

Collibra AI Governance extends the Collibra Data Intelligence Platform with AI use case catalogs, model documentation, policy management, and stakeholder workflows. The product surface is the metadata layer over data and AI assets. Inline policy enforcement at the AI request boundary sits at a different architectural layer. This article walks through what Collibra covers, where the boundary ends, and how the two layers fit together.

ai-governancecollibradata-catalogai-control-planemetadata
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AI Security Tools List: The 14 Categories That Actually Show Up in Enterprise Architecture

The AI security category is fragmented across 14 distinct tool types: AI gateway / policy enforcement, AI DLP, AI SPM, model security, guardrails, agent identity, red teaming, model risk management, AI observability, vendor risk for AI, AI incident response, AI training data security, federated learning security, and AI supply chain. Each category solves a different layer of the AI stack. Buyers who treat the category as one bucket overspend on overlap and underspend on the actual enforcement layer. This list walks through what each category does, where it sits in the architecture, and what to ask vendors before buying.

ai-securityvendor-evaluationbuying-guideai-toolscategory-mapai-gateway
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