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AI Security Posture Management (AI SPM) for MLOps and Agent Pipelines

Discover every model, agent, and MLOps pipeline running across your environment. Score risk, close compliance gaps against 43 global compliances, and generate an AI Bill of Materials automatically.

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Common AI Compliance & Security Challenges

Compliance Challenges

Compliance Challenges

Adhering to industry and regulatory standards is quite complex.

Lack of Visibility

Lack of Visibility

Organizations struggle with monitoring AI/ML pipelines for security risks.

Misconfigurations

Misconfigurations

Applications, Models, Workloads and environment often lack proper security controls.

AI Model Vulnerabilities

AI Model Vulnerabilities

AI models face threats like adversarial attacks, data poisoning, and unauthorized access.

Data Security Risks

Data Security Risks

Sensitive data can be exposed during AI model training and inference.

Is Your AI Risk Free & Compliant ?

Check Our Solution

Agentic AI Security Platform

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Adopt AI Fearlessly. Stay Fully Protected.

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Prompt Firewall

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Real-time protection against prompt injections, jailbreaks, data leaks, and harmful outputs.

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AI Red Teaming

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Continuously test LLMs and AI systems with adversarial attacks to uncover risks before they’re exploited.

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AI Detection & Response (AI-DR)

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Detect, investigate, and respond to AI threats in real time with automated remediation.

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Securing AI Factories

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Secure the entire AI factory—from data, training pipelines, and models to deployment environments.

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AccuKnox AI Copilot

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Get instant Al security insights, automate investigations with AskAI.

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Agentic AI Security

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Secure autonomous AI agents and MCP ecosystems from abuse, over-permissioning, and unsafe actions.

AI Security Platform Tour
(Models, Agents, MCPs, SDKs)

  • Inventory View
  • AI Model View
  • Managed Agents View
  • Unmanaged Assest Discovery
  • Prompt Firewall Dashboard
  • Runtime Defense
  • Runtime Agent Sandboxing
  • AI Compliance

Inventory View (List)

Asset sprawl across cloud, network, and API layers means no single source of truth for what is exposed and how it is configured.

AI Assets Covered

AI Models, Datasets, Compute Resources, Bedrock, SageMaker, Vertex AI, Azure AI, and cloud AI services.

Security Outcomes

AI Asset Inventory, AI Security Posture Management (AI-SPM), risk visibility, ownership tracking, and exposure management.

Inventory View

Risk View from Red Teaming

Per-model risk view from automated red team scans, with findings broken down across four attack categories: code, hallucination, prompt injection, and sentiment analysis. Each category lists severity counts and specific test cases.

Security Tests

Prompt Injection, Hallucination Detection, Jailbreak Testing, Unsafe Code Generation, and adversarial robustness validation.

Framework Mapping

OWASP Top 10 for LLMs, MITRE ATLAS, Model Security Scoring, and risk-based remediation.

AI Model View

Managed Agents View

Managed agent inventory filtered by cloud provider, with a detail pane for agent metadata, memory configuration, deployment timeline, and risk finding counts by severity.

Agent Coverage

Amazon Bedrock Agents, Azure AI Agents, Copilots, Custom Agent Frameworks, and enterprise agent deployments.

Operational Visibility

Agent Memory, Tool Usage, Knowledge Bases, Deployment Lifecycle Monitoring, and runtime risk insights.

Managed Agents View

Inventory View (List)

Shadow AI discovery lists unmanaged assets by category: AI agents, AI gateways, AI inference engines, AI-ML libraries, AI SDKs, and MCP servers. Each category shows asset count and associated findings.

Shadow AI Detection

MCP Servers, AI SDKs, AI Gateways, Inference Engines, AI/ML Libraries, and unknown AI assets.

Discovery Insights

Software Provenance, License Tracking, Security Findings, Asset Ownership, and dependency visibility.

Unmanaged Assets Discovery

AccuKnox Prompt Firewall Dashboard

Prompt the firewall dashboard with top policy violations by policy name and by application, failure counts by severity, and a full application inventory with violation trends and owner attribution.

Threat Detection

Prompt Injection, Secrets Exposure, PII Leakage, Toxicity Violations, and unsafe prompt activity.

Runtime Controls

Prompt Firewall, AI Gateway Enforcement, Response Filtering, Policy Analytics, and usage monitoring.

Prompt Firewall Dashboard

Runtime Defense Prompt Policies

Prompt policy library with 10 policy types: anonymisation, gibberish detection, prompt injection, toxicity, competitor mentions, topic bans, code filtering, language enforcement, regex sanitisation, secrets detection, and token limits.

Policy Library

Anonymization, Secrets Detection, Toxicity Controls, Code Restrictions, Token Limits, and language enforcement.

AI Guardrails

Runtime Enforcement, Responsible AI Controls, Content Governance, Risk Reduction, and policy compliance.

Runtime Defense

Runtime Agent Sandboxing

KubeArmor runtime sandboxing for AI agents with auto-discovered and custom policies. Each policy enforces process, filesystem, credential, and network isolation at the K8s workload level via zero-trust YAML.

Zero Trust Controls

Process Isolation, Filesystem Protection, Network Segmentation, Credential Security, and execution control.

Protected Workloads

AI Agents, MCP Servers, Inference Engines, Kubernetes Agentic AI Environments, and autonomous workflows.

Runtime Agent Sandboxing

AI Compliance, Attack Frameworks

Compliance framework selector with 12 active standards: OWASP Top 10 for LLMs, MITRE ATLAS, NIST 800-171, HIPAA, ISO 27001, SOC 2, PCI, RBI CSF, HITRUST CSF, GDPR, ISO 27017, and NIST SP 800-53.

Compliance Standards

OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO 27001, SOC 2, GDPR, and HIPAA.

Governance Coverage

AI Models, Agents, Datasets, Prompts, Infrastructure, Audit Readiness, and control validation.

AI Compliance
Fireside-featured

Fireside Chat: Buyer and Creator's Perspective of Agentic AI Risks

Understand real-world enterprise AI deployments, active AI threat vectors, AI gateways, runtime enforcement, and the emerging agentic AI attack surface.

Watch This Webinar
  • Flexible AI Deployments
  • Support Matrix
  • Security Across Layers
AI-SPM security posture
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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-SPM – SaaS & On-Prem Deployment

Same powerful protection. Choose the deployment that fits your infrastructure, data-residency, and compliance needs.

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AccuKnox Stateful Prompt Firewall and Why Per Prompt Inspection Fails Against Multi Turn Attacks

Multi turn attacks reach 96.2% success against frontier AI models. A firewall that scores messages in isolation never sees the session. Here is what stateful inspection changes.

Read Blog

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

Multi-Cloud Agent Visibility & Auditing

Continuous discovery, behavioral auditing, and risk monitoring of AI agents across cloud environments.

Sandbox Unsafe Tool Usage

Sandbox Unsafe Tool Usage

Prevents agents from executing risky external tools, APIs, and actions in runtime workflows.

Sandbox Auto-Generated Code

Sandbox Auto-Generated Code

Isolates LLM-generated scripts and code execution to prevent malicious runtime behavior.

Multi-Platform Support

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.
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AI Security Use Cases

Prompt Firewall

Prompt Firewall

  • Prompt Injection Defense
  • PII & Secrets Redaction
  • Toxicity Filtering
  • Code Execution Prevention
AI Red Teaming

AI Red Teaming

  • Supply Chain Security (Malicious payloads)
  • Prompt Leakage Risk (Hardcoded secrets)
  • License Compliance (Restrictive licenses)
  • Bias & Toxicity Detection
AI Cloud Infra Security<

AI Cloud Infra Security

  • Exposed Notebooks (Public access)
  • Unencrypted Training Data
  • Over-Permissive Roles (IAM risks)
  • Shadow Al Assets (Unapproved instances)
Model Sandboxing

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

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

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
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Talk to Security Experts

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Ready to Protect Your Sensitive Cloud Assets?

On-Demand Reports for AI Security

AISPM Reports

AI Security Key Differentiators

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Runtime prompt firewall with LLM-as-judge sanitization and blocking

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Automated AI red teaming for injections, hallucinations, toxicity, bias

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Multi-layer AI security across models, agents, datasets, and pipelines

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AI asset inventory with agent, model, and pipeline lineage mapping

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AI detection and response for model misuse and infra misconfigurations

LLM Security eBook

Detect and block AI-specific threats via model red-teaming, prompt filtering, dataset integrity checks, and secure ML supply-chain controls.

Get AI Security eBook

AI Security Competitive Stack Ranking

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AI Security Resources

Securing AI Factories

Securing AI Factories with AccuKnox

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AccuKnox AI Security: Differentiated AI Security

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Introducing AccuKnox AI CoPilot

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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Ready For A Personalized Security Assessment?

“Choosing AccuKnox was driven by opensource KubeArmor’s novel use of eBPF and LSM technologies, delivering runtime security”

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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.”

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Manoj Kern

CIO

“Tible is committed to delivering comprehensive security, compliance, and governance for all of its stakeholders.”

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Merijn Boom

Managing Director

Trusted by Cybersecurity Leading Investors

10+

Patents

Registered with United States Patent & Trademark Office

2M+

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Downloads

stars

1,000+

5

Books Published

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$15m

Seed Funding

Featured Customers

aliceblue us-dod purestorage idt sonesta nask prudent

Awards & Recognitions

top10 nasscom purestorage neapp silicon india tie cybertech 5g-lab bsides

Investors

sri mdsv capital nationalgrid avanta ventures dreamit 5g-open-innovation-lab dolby family z5-capital outliers

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.

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