Unified Zero Trust CNAPP 
Secure APIs 
Secure Kubernetes 
Secure Secrets 
Defend Against Shadow AI and Prompt Attacks with AI-SPM Red Teaming
Address modern AI Threats Zero Trust prompt and Sandboxing
Common AI Compliance & Security Challenges
Compliance Challenges
Adhering to industry and regulatory standards is quite complex.
Lack of Visibility
Organizations struggle with monitoring AI/ML pipelines for security risks.
Misconfigurations
Applications, Models, Workloads and environment often lack proper security controls.
AI Model Vulnerabilities
AI models face threats like adversarial attacks, data poisoning, and unauthorized access.
Data Security Risks
Sensitive data can be exposed during AI model training and inference.
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Use Cases of our AI Security Platform
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AI Governance, Risk, and Compliance (AI-GRC)
- Supports NIST AI, MITRE AI, AISCP, SOC, and more.
- Automates audit trails and policy checks.
- Enhances visibility and control over AI governance.
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Automated Red Teaming
- Dynamically tests AI models for vulnerabilities.
- Automated adversarial attack simulation to proactively identify weaknesses.
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LLM Prompt Firewall
- Protects against prompt injection attacks.
- Ensure safe and controlled interactions in LLM-based applications.
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Training Pipeline Security
- Secures model training pipelines and artifacts.
- Safeguards trained AI models from theft, tampering, or malicious alterations.
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Data Security
- Detecting PII/PHI exposure.
- Prevents dataset tampering.
- Prevents unauthorized access.
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AI Security Modules
AI Security Posture Management (AISPM)
AI Deployment Security
AI Data Security
AI Runtime Security
AI Red Teaming
AI Governance & Compliance
AI Agent Security Across Multi-Cloud Platforms
Real-time visibility, sandboxing, and auditing for AI agents across Azure AI Foundry, Copilot Studio, and AWS Bedrock.
Multi-Cloud Agent Visibility & Auditing
Continuous discovery, behavioral auditing, and risk monitoring of AI agents across cloud environments.
Sandbox Unsafe Tool Usage
Prevents agents from executing risky external tools, APIs, and actions in runtime workflows.
Sandbox Auto-Generated Code
Isolates LLM-generated scripts and code execution to prevent malicious runtime behavior.
Multi-Platform Support
Industry-first agent discovery and governance across major cloud platforms.
AI Model Cards for Continuous Governance
Transform your model documentation from static reports into a real-time security and risk dashboard.
- Continuous Security & Supply Chain
Get a live Software Bill of Materials (SBOM), real-time vulnerability scanning, and ongoing license compliance checks for all model components. - Automated Validation & Risk Scoring
Use sandbox-driven assessments for automated red teaming, evaluating safety, bias, toxicity, jailbreak resilience, and assigning a dynamically changing risk score. - Runtime Observability & Fencing
Establish behavior baselines and monitor operational activity to detect policy violations and ensure real-time data isolation and fencing of model data stores.
AI Security Use Cases
Prompt Firewall
- Prompt Injection Defense
- PII & Secrets Redaction
- Toxicity Filtering
- Code Execution Prevention
AI Red Teaming
- Supply Chain Security (Malicious payloads)
- Prompt Leakage Risk (Hardcoded secrets)
- License Compliance (Restrictive licenses)
- Bias & Toxicity Detection
AI Cloud Infra Security
- Exposed Notebooks (Public access)
- Unencrypted Training Data
- Over-Permissive Roles (IAM risks)
- Shadow Al Assets (Unapproved instances)
Model Sandboxing
- Agentic Network Isolation (API restrictions)
- File System Protection (Read-only paths)
- Process Whitelisting (Block sub-shells)
- Data Exfiltration Control (DNS filtering)
AI Detection & Response
- Al activity monitoring across cloud and models
- Policy-based anomaly detection
- Real-time alerts and automated remediation
- Full audit trail of Al actions
AI-Based Ticket Creation
- Automatic ticket creation from Al security alerts
- Context-rich tickets with evidence and metadata
- Integration with Jira and ServiceNow
- Workflow-driven remediation tracking
AccuKnox AI-SPM Delivers Security At Every Layer of AI
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Ready to Protect Your Sensitive Cloud Assets?
Unified, Secure, and Flexible Al Deployment
Deploy your Al workloads anywhere-from on-prem to multi-cloud-with enhanced security, seamless orchestration via MCP, and support for the widest ecosystem of Al/ML platforms.
AI Security Key Differentiators
Runtime prompt firewall with LLM-as-judge sanitization and blocking
Automated AI red teaming for injections, hallucinations, toxicity, bias
Multi-layer AI security across models, agents, datasets, and pipelines
AI asset inventory with agent, model, and pipeline lineage mapping
AI detection and response for model misuse and infra misconfigurations
Detect and block AI-specific threats via model red-teaming, prompt filtering, dataset integrity checks, and secure ML supply-chain controls.
AI Security Competitive Stack Ranking
AI Security for AI/LLM Workload Security FAQs
AccuKnox's ModelKnox provides real-time runtime visibility and threat detection designed specifically for AI workload behaviors. It identifies inference manipulation, model extraction, and resource abuse in milliseconds, then triggers automated remediation that reduces response times by 95%.
Yes. AccuKnox correlates signals across prompt inputs, model behavior, API calls, and runtime anomalies to reconstruct multi-stage attack paths. Cross-layer visibility from AI-SPM, runtime monitoring, and API security lets teams detect chained attacks before they escalate.
AccuKnox provides runtime observability with process-level visibility, generating execution lineage across containers, agents, and AI pipelines. It maps relationships between prompt, model, tool or API calls, and system actions, enabling full audit trails and behavior timelines.
AccuKnox's CDR capabilities automate remediation for AI security incidents, cutting response times by 95% through intelligent automation built specifically for AI workloads. Incident response triggers without manual intervention, containing threats before they cause downstream damage.
AccuKnox ModelKnox delivers real-time threat detection tuned for AI workload attack patterns including prompt injection, output manipulation, and model extraction. Traditional security tools lack the inference-layer visibility required at millisecond response windows.
AccuKnox uses behavioral monitoring and runtime threat detection to identify novel attack patterns against AI workloads before they cause damage. Because it analyzes behavior rather than signatures, it catches unknown threats that exploit hidden weaknesses in models or training data.
AccuKnox offers AI-SBOM and AIBOM-based visibility into models, libraries, and dependencies. It continuously scans for vulnerabilities and malicious packages, detects anomalous behavior from compromised dependencies, and enforces trusted registries and signed artifacts across LLM frameworks like LangChain.
AccuKnox secures data pipelines from ingestion through training, with visibility and controls across datasets, training processes, and model outputs. It detects training data poisoning and dataset manipulation that remain undetected throughout conventional development lifecycles.
ModelArmor provides runtime sandboxing and isolation for AI workloads using eBPF technology. It protects production LLM environments from model extraction, inference manipulation, and resource abuse with kernel-level enforcement that adds no agent overhead to inference pipelines.
AccuKnox secures the complete AI lifecycle from data ingestion through deployment, applying phase-appropriate controls. Training gets data poisoning detection and pipeline governance. Inference gets prompt firewall, output monitoring, and behavioral anomaly detection.
AccuKnox integrates with KubeArmor to provide comprehensive policy enforcement across Kubernetes clusters with AI-specific runtime controls. It handles both container orchestration security and AI workload-specific requirements within a unified policy framework.
AccuKnox provides consistent LLM protection across AWS, Azure, GCP, and hybrid environments with unified policy enforcement and compliance monitoring. Security posture remains uniform regardless of where models are deployed, eliminating gaps that emerge from per-cloud tooling.
AskADA AI co-pilot integrates threat intelligence feeds with real-time analysis, delivering contextual security insights for AI-specific threats and vulnerabilities. It surfaces relevant intelligence without requiring security teams to manually correlate across feeds.
AccuKnox's AI-powered correlation reduces false positives by 95% through intelligent analysis tuned specifically for AI and LLM workload patterns. It distinguishes legitimate AI operations from genuine threats rather than generating noise that overwhelms security teams.
ModelKnox delivers unified dashboards providing visibility, risk management, and compliance tracking across all AI assets. Security teams get a single pane covering posture, vulnerabilities, policy violations, and compliance status rather than stitching together fragmented tools.
AskADA provides contextual security insights and automates compliance checks against NIST AI RMF, EU AI Act, OWASP, AVID, and MITRE simultaneously. It surfaces regulatory gaps and generates unified reporting so teams spend less time on manual audit preparation.
AccuKnox provides specialized whitepapers, AI governance checklists, threat analysis reports, and implementation guides addressing unique AI and LLM threats. Resources cover AI-SPM tooling, governance frameworks, and secure AI workload deployment for practitioners who need more than generic documentation.
AccuKnox discovers the full inventory of internally developed agents, models, and pipelines across environments. It maps agent capabilities, data access, and tool integrations, then applies policy-as-code governance across the build, deploy, and runtime lifecycle with continuous behavioral monitoring.
AccuKnox enforces prompt firewall rules across 12+ categories globally across all models and agents, with customization for business-specific guardrails. Policy engines validate actions before execution at the prompt, model, API, and runtime layers with continuous red teaming for evolving behaviors.
AccuKnox uses a sandboxing approach to understand agent application behavior at runtime. It analyzes behavioral patterns to infer intent, evaluates effective versus required permissions to identify overreach, and enforces least privilege with just-in-time access controls for NHIs.
AccuKnox detects PII, API keys, credentials, and other sensitive data in both prompts and model responses using pattern matching combined with contextual classification. It supports configurable actions including monitor, alert, or block, covering data in transit and generated outputs.
AccuKnox tackles adversarial attacks through AI-SPM with runtime monitoring and behavioral analysis designed specifically for LLM threat patterns. Automated red teaming runs continuous adversarial simulations to test model defenses and adapt security postures in real time.
AccuKnox features a Prompt Firewall for LLMs that guards against injection attacks and enforces safe, auditable prompt interactions. It applies configurable policies across all connected models and agents, blocking injection attempts before they reach model inference.
AccuKnox integrates with GitHub Actions and other CI/CD pipeline tools, enabling security scanning throughout AI development lifecycles. DevSecOps teams get LLM security embedded into existing workflows without disrupting model deployment velocity.
AccuKnox recommends assessing vendor data handling practices, model behavior, and access controls, requiring transparency through AIBOM, audit logs, and compliance mappings against EU AI Act and ISO 42001. Continuous runtime monitoring of third-party access post-deployment is essential.
AccuKnox performs continuous AI asset discovery across endpoints, browsers, SaaS, and cloud environments. It detects shadow AI usage by analyzing outbound traffic, API calls, and browser interactions, correlating usage with user identity and data access patterns to assess risk.
AccuKnox identifies embedded AI capabilities within SaaS platforms including copilots, plugins, and third-party integrations. It analyzes application behavior, API calls, and data flows to uncover hidden AI usage, then flags unauthorized integrations based on governance policies.
AccuKnox provides ModelArmor as an open-source solution that securely isolates AI and ML workloads with sandboxing built on KubeArmor technology. Organizations avoid vendor lock-in while leveraging community-driven AI security innovations customizable for specific deployment needs.
AccuKnox's agentless AI-SPM provides comprehensive risk assessment through API integrations without installing software on AI infrastructure. It maintains inference performance while ensuring security posture visibility, eliminating the attack surface and overhead that agent-based approaches introduce.
AccuKnox's Zero Trust AI Security framework ensures continuous verification and policy enforcement across the entire AI lifecycle within its integrated CNAPP architecture. Every agent, model, and API interaction is verified rather than assumed trusted, regardless of where it runs.
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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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About Us
AccuKnox delivers a Zero Trust Security platform for AI, API, Application, Cloud, and Supply Chain Security. Incubated out of R&D innovator, SRI International (Stanford Research Institute), AccuKnox holds seminal Zero Trust security patents and is backed by top-tier investors including National Grid Partners, Dolby Family Ventures, Dreamit Ventures, Avanta Ventures, and the 5G Open Innovation Lab.