AccuKnox (vs) Netskope

Security Graph pipeline view. Agents mapped to linked models, knowledge bases, tools. Discovers on- prem inference engines and their hosted models. Covers LLMs and ML models. Reference

Browser plugin (Chrome + Firefox), CLI, API, AI Gateway integration modes. Reference

AccuKnox detects self-hosted inference engines (Ollama, vLLM) and local AI deployments with workflow alerts. Netskope tracks SaaS AI app usage via CASB. Reference

Upload custom domain-specific adversarial prompts. Automated red-teaming with configurable attack categories. Reference

Evaluate models on: provenance, adversarial robustness, supply chain, AIBOM, model file security (pickle, serialization/deserialization). Reference

Static security analysis for traditional ML models beyond LLMs. Reference

SBOM, CBOM, AIBOM, HBOM: generate, create, compare, sign, verify. Supports CERT-In guidelines. Reference

Detects poisoning in locally deployed inference engines (Ollama, vLLM). ModelArmor open-source tool. Reference

Full pipeline visibility: training data, model training, serving infrastructure. Security Graph maps dependencies across the AI stack. Reference

ModelArmor OSS + enterprise. Semantic detection across custom sub-prompt categories. SDK, API, and Gateway modes. Netskope uses DLP-based inline inspection only. Reference

Session-level abuse detection, jailbreak pattern recognition, multi-turn attack detection. Netskope limited to basic DLP pattern matching. Reference

Output filtering, toxicity detection, custom content categories. Netskope limited to DLP-based output inspection. Reference

OpenClaw: autonomous agent sandboxing using KubeArmor-based eBPF enforcement. Isolates agent tool calls and file system access. Reference

Sandbox MCP server tool executions. Prevent unauthorized system calls from MCP-connected tools. Reference

Isolate untrusted model inference in sandboxed environments with restricted system access. Reference

Protect training data from poisoning. Protect RAG datasets from poisoning. Fence sensitive data from RAG retrieval. Reference

AI asset threat detection rules for regional settings. Netskope supports data residency via CASB controls. Reference

Consume AWS cloud logs, detect threats on AI assets (public exposure, unauthorized access, config drift), auto-remediate. Reference

Azure-specific AI asset threat detection rules and automated response. Reference

Auto-remediate: change exposed assets to private, alert data owners/ custodians/ security teams, create ITSM tickets (Jira, ServiceNow). Netskope limited to alert-based notifications. Reference

Azure AI Foundry, AWS API Management, Apigee, Bifrost, LiteLLM. Netskope uses inline proxy with no dedicated AI gateway connectors. Reference

Direct integration with Microsoft Copilot Studio and Power Apps for enterprise AI workflows. Reference

Native integration with AWS Bedrock AgentCore for securing AWS-hosted AI agents. Reference

SDK, Browser Plugin (Chrome + Firefox), CLI, API, Gateway proxy. Wider AI-specific integration surface than Netskope CASB connectors. Reference

Dedicated AI Factory security solution for NVIDIA RunAI environments. Beta docs coming soon. Reference

Isolate GPU workloads across tenants in shared AI infrastructure. Beta docs coming soon. Reference

Control and audit access to CUDA runtime and GPU resources. Beta docs coming soon. Reference

Full on-prem, air-gapped, edge, single-node deployment options. Netskope AI security is SaaS-only. Reference

SaaS, on-prem, hybrid, MSSP multi-tenant. Netskope offers SaaS with NewEdge PoPs but no on-prem AI security option. Reference

Scan agent artifacts (tool definitions, schemas, configurations) for security risks before deployment. Reference

Adversarial testing of AI agent behaviors: tool misuse, privilege escalation, data exfiltration through agentic workflows. Reference

Inventory and assess security posture of deployed AI agents, their tool access, and permission scopes. Reference

Runtime monitoring and enforcement for autonomous AI agent actions. eBPF-based observability via KubeArmor. Reference

Trace and log AI agent decision chains, tool invocations, and data flows for audit and forensics. Reference

Identity and access management for AI agents. Control which agents can access which tools and data sources. Reference

Protect endpoints where AI agents execute: local machines, CI/CD runners, cloud VMs. Reference

Centralized gateway for AI agent traffic. Policy enforcement for agent-to-tool and agent-to-model communications. Portkey integration. Reference