Unified Zero Trust CNAPP 
Secure APIs 
Secure Kubernetes 
SIEM 
Secure Secrets 
AccuKnox (vs) Straiker
Parameters
Straiker
OnPrem Support
- Full on-prem deployment via single-node or managed install (EKS, AKS, GKE
- Air-gapped infrastructure supported; SaaS and on-prem share the same feature set
- AWS AMI-based control plane install available
- SaaS / vendor-hosted API only — no documented on-prem, air- gapped, or self-managed control plane
- Deploys via single-line API, SDK, webhook, or AI Sensor (Kubernetes); eBPF sensor option
- Available through AWS Marketplace (AI Agents & Tools category)
AI Platform Security (AI-SPM)
- Multi-cloud AI inventory across AWS, Azure, GCP, and on-prem from one console
- Auto-mapping of deployed AI apps with security graph view and AI-aware policy evaluation with automated remediation
- 33+ compliance frameworks including ISO 27001, OWASP, and AVID mapped natively
- Discover AI delivers AI-SPM and self-coined “Agent-SPM" — agent inventory, posture, misconfig & risky-permission detection
- Focused on the agent/MCP layer (Bedrock AgentCore, Azure Foundry, Copilot Studio) rather than full multi-cloud AI asset inventory
- Maps findings to OWASP Agentic Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act
AI Pipeline Security
- AI/ML pipeline graph view tracks data flow from model to endpoint
- Secrets scanning and IaC scanning integrated into pipeline runs
- Native CI/CD integrations: Jenkins, GitHub Actions, Azure DevOps, Harness, AWS CodePipeline
- Ascend AI runs adversarial testing as a native CI/CD stage (their “AgentSecOps" discipline) on every build/ prompt/ config change
- Focus is behavioral red-teaming in pipeline, not secrets/IaC scanning or model-to-endpoint data-flow graphing
- No documented pipeline secrets or infrastructure-as-code scanning
Model Security
- Static scanning of LLM and ML model files: Pickle, TensorFlow SavedModels, GGUF, DDUF formats
- Runtime model execution visibility and protection via KubeArmor (eBPF)
- Supply chain poisoning detection for models sourced from public repositories
- Behavioral/runtime model testing — jailbreaks, evasion, Model Confusion Index — via Ascend AI
- No documented static model-file scanning (Pickle / SavedModel / GGUF / safetensors)
- Model risk treated at the language/ behavior layer, not at the artifact/ supply-chain file level
Dataset Security
- PII and PHI scanning of datasets at rest with tenant-specific custom scan configurations
- Data poisoning detection covering weights and biases integrity
- Supports regulated data environments requiring HIPAA and SOC 2 controls"
- Runtime PII/PHI detection, redaction & tokenization in agent prompts/ responses/ traces (before logs & vector DBs)
- Data poisoning / memory poisoning called out as threats but addressed at runtime, not via dataset-at-rest scanning
- No documented scanning of training datasets at rest
Runtime Security
- Zero Trust runtime enforcement at process, file, network, and capabilities level via eBPF (KubeArmor)
- Behavior baselining with real- time anomaly detection across K8s, VMs, bare metal, serverless
- No dependency on iptables or kernel modules
- Defend AI provides runtime security for AI agents — semantic detection at <300ms, eBPF Sensor + AI Sensor for K8s
- Scoped to agent/LLM interaction traffic (prompts, tool calls, traces), not OS-level process/ file/ network Zero Trust enforcement
- Self-learning models; inline block / shape / sanitize
Prompt Firewalling
- Inline Prompt Firewall deployed at the AI gateway layer with real- time prompt and response inspection
- Blocks prompt injection, jailbreaks, PII/PHI leaks, and unsafe content before reaching the model or user
- Configurable block, alert, and redact policies per application
- Defend AI is exactly this — inline runtime blocking of direct + indirect prompt injection, data leakage, tool manipulation
- Multimodal inspection (text, code, images, audio, files), claimed 98.1% accuracy at <300ms
- Deploy via API / SDK / webhook / MCP server / AI Gateway with configurable block-shape- sanitize
Safety Guardrails — Session Abuse
- Session-level monitoring with real-time visibility into prompt history and user behavior patterns
- Jailbreak and prompt injection detection with per-session policy enforcement
- PII/PHI leak prevention in both prompt and response traffic
- Detects instruction manipulation, tool misuse, reconnaissance, and resource exhaustion per agent run
- User Behavior Analytics flags anomalous agent usage; Chain of Threat forensics traces every user↔model↔tool step
- Per-session enforcement with stateful tracking of identity, permissions, and session history
Safety Guardrails — Unsafe Content
- Safety guardrails coversentiment analysis, hallucination flagging, and code injection detection
- Outputs blocked or flagged based on configurable OWASP- aligned rule sets
- Works across cloud-hosted and on-prem LLM deployments
- Application grounding & output safety — suppresses toxic output, harmful content, drift, and policy violations
- Multimodal + multi-language; hallucination/grounding validation before responses reach users
- Cloud-hosted only (no on-prem LLM deployment option)
Red Teaming
- Automated LLM red teaming using adversarial probes: hallucination, code injection, prompt injection, toxicity, jailbreaks
- ML static scans for model file vulnerabilities including Pickle exploits
- Produces an LLM Security Card with risk scoring and remediation workflow
- Ascend AI — their flagship strength: continuous, autonomous, 24/7 agentic red- teaming across tools, MCP servers, workflows
- Proprietary STAR taxonomy + Autonomous Attack Simulation + LAVA attacks; Attack Success Rate metric; STRIDE modeling
- Native CI/CD gating; maps to OWASP/MITRE ATLAS/ NIST/ EU AI Act; “industry's highest attack success rate" claim
Attack Detection (AI-DR)
- AI-DR ingests cloud logs (CloudTrail, Azure Logs) and flags risky AI resource creation against security baselines
- AI misuse detection across compute, model, and data planes with real-time alerts
- Shadow AI detection discovers unapproved notebooks, models, and AI services across AWS, Azure, GCP
- Detection engine analyzes agentic traces & spans for goal hijacking, tool misuse, identity misuse, supply-chain, resource exhaustion
- Trace-based runtime detection (>98% accuracy, sub-second), NOT cloud-log-based AI resource/ misuse detection
- Shadow-AGENT discovery (Discover AI) but no CloudTrail/ Azure-log AI-DR across cloud planes
Incident Response
- Automated remediation removes public access from misconfigured AI resources
- CDR-based response workflows for AWS, GCP, and Azure
- Ticketing integration via ServiceNow and Jira for remediation tracking
- Chain of Threat forensics reconstructs full attack narratives; inline blocking + alerts via Slack/ email/ webhook
- No documented automated cloud remediation (e.g., removing public access) or CDR workflows
- No documented ServiceNow/ Jira ITSM ticketing integration
AI Gateway Integrations
- Native integrations with Azure APIM, AWS API Gateway, LiteLLM, and Bifrost AI
- Prompt Firewall deploys inline at the gateway layer — no model- side changes required
- Supports multi-provider routing scenarios out of the box
- Deploys inline at an AI Gateway / Proxy layer integrates with Portkey for multi-provider routing
- Enforcement via API / SDK / webhook / MCP server with no model-side changes
- Gateway coverage centered on agent traffic fewer named cloud-APIM integrations than AccuKnox
SDK and Platform Integrations
- Python SDK for direct application-level Prompt Firewall onboarding
- Pre-built integrations for Azure Copilot Studio, Bedrock AgentCore, and Microsoft Power Apps
- Full support matrix documents supported platforms, versions, and configurations
- Single-line SDK/API onboarding very broad agent-ecosystem coverage
- Integrations Cursor, Claude Code, GitHub Copilot, Windsurf, MS Copilot, ChatGP Enterprise, Agentforce, Bedrock AgentCore, Azure AI Foundry, Copilot Studio, Slack AI, Box, Google Drive, LangChain
- Open elemetry-based SDK + AI Sensor for Kubernetes
Agentic AI and MCP Security
- SPIFFE-based workload identity for AI agents across multi-cloud and heterogeneous deployments
- OpenFGA for fine-grained authorization with upstream caller sequence tracking
- MCP tool sandboxing with least- permissive access enforcement and auto-discovery of AI agents and MCP servers
- Deep agentic/MCP specialist MCP discovery, proprietary MCP hreat Database, tool-poisoning/ rug-pull/ output-in ection prevention, A2A coverage
- Runtime least-privilege enforcement + audit logs on agent-tool calls tool poisoning is the #1 attack vector
- Behavioral/ semantic approach — no SPIFFE workload identity or OpenFGA fine-grained authorization
Deployment Flexibility
- Supports SaaS, on-prem, air- gapped, public cloud, private cloud, and edge/IoT
- Available on AWS, Azure, Red Hat, and Oracle Cloud Marketplaces
- SaaS and on-prem deployments documented with hardware prerequisites and architecture overview
- SaaS / vendor-hosted API AWS Marketplace AI Agents & Tools API / SDK / webhook / AI Sensor K8s / eBPF sensor
- No on-prem, air-gapped, private-cloud, or edge/Io deployment documented
- Single-cloud- marketplace presence AWS vs AccuKnox's four marketplaces
Why Customers Choose AccuKnox Over Straiker
Better
AccuKnox offers superior protection across cloud, containers, and Kubernetes environments, supporting over 45 compliance frameworks and enhanced by open-source innovations like KubeArmor, trusted by over 1 million downloads.
Faster
AccuKnox speeds up security operations with real-time runtime protection, cutting remediation time by 91% and reducing false positives by 89%, making threat detection and response significantly more efficient.
Cheaper
AccuKnox delivers a unified Cloud Native Application Protection Platform (CNAPP) that lowers total cost of ownership by consolidating multiple security tools into one solution, offering flexible pricing that scales seamlessly for organizations of all sizes.
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“Choosing AccuKnox was driven by opensource KubeArmor’s novel use of eBPF and LSM technologies, delivering runtime security”
Golan Ben-Oni
Chief Information Officer
“At Prudent, we advocate for a comprehensive end-to-end methodology in application and cloud security. AccuKnox excelled in all areas in our in depth evaluation.”
Manoj Kern
CIO
“Tible is committed to delivering comprehensive security, compliance, and governance for all of its stakeholders.”
Merijn Boom
Managing Director
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DevSecOps & Security Teams Love our AppSec/CloudSec/AISec Platform
“AccuKnox allows Public Sector agencies and entities to protect themselves against current and emerging threats.”
Natalie Gregory, Vice President Enterprise Solution
Looking to Migrate from Straiker?
Evaluate how AccuKnox stands apart from Straiker based on key features, pros and cons. We have compiled a list of solutions that leading organizations compare while considering AccuKnox as a potential Tenable alternative. While analyzing AccuKnox and Straiker side by side you can differentiate competencies, integration, deployment, service, support, and specific product capabilities that will influence your purchasing decision.
AccuKnox Zero Trust CNAPP
“I had a very good initial conversation with the sales team and had a successful demo. The solution is very capable.”
Manager, Tech Services/Infosec - Healthcare and Biotech
AccuKnox Zero Trust CNAPP
“I really like the zero-trust architecture of the product. It gives the strong visibility and control across the cloud native workload as it is a built-in security model.”
IT Manager - Services (non-Government)
AccuKnox Zero Trust CNAPP
“Working with AccuKnox Zero Trust CNAPP was a great experience. It was a seamless integration with our cloud infrastructure.”
Director, Information Security - Banking
AccuKnox Zero Trust CNAPP
“I am quite impressed by the product and believe it’s currently the only fit for all my worries over the cloud.”
CISO - Banking
AccuKnox Zero Trust CNAPP
“Real-time security for my cloud native application. This solution is a huge benefit for any emerging threats and identifying vulnerabilities.”
CISO - Banking