AI Security¶
Core AI Security & Platform¶
1. How does AccuKnox detect and prevent adversarial and zero-day attacks on LLMs?
AccuKnox's AI Security applies AI-SPM with runtime behavioral monitoring, syscall tracing, and anomaly detection to identify adversarial patterns and zero-day exploits against LLM inference pipelines in real time.
2. How does AccuKnox enforce policy controls for AI workloads in Kubernetes?
Using KubeArmor integration, AccuKnox enforces fine-grained eBPF/LSM policies (process, file, and network) for AI workloads running in Kubernetes clusters, ensuring least-privilege enforcement and runtime protection.
3. How does AccuKnox secure AI data pipelines against poisoning and compliance risks?
AccuKnox provides end-to-end visibility from data ingestion to training with dataset lineage tracking, model SBOMs, and automated compliance checks (NIST AI RMF, EU AI Act, HIPAA), blocking poisoned datasets and ensuring regulatory adherence.
4. Does AccuKnox AI security cover on-premise deployed AI components?
Yes. AccuKnox provides comprehensive AI security for a wide range of deployment models. Our platform is architected to be flexible, offering robust protection for: + Public and Private Clouds + Fully On-Premise Environments + Air-Gapped Infrastructure + Hybrid Deployments This ensures your AI models, data, and infrastructure are secured, regardless of where they are deployed.
5. How does AccuKnox secure MCP servers?
AccuKnox secures Master Control Program (MCP) servers by integrating them into our Zero Trust security framework. The MCP server operates within a Sandboxed Execution environment, which provides process and network isolation. All interactions are governed by strict authorization controls and are continuously monitored, ensuring the integrity and security of the core AI orchestration layer.
6. How does AccuKnox secure AI Agents?
AccuKnox secures AI Agents through advanced runtime sandboxing and policy enforcement. Key security measures include: + Sandboxing Unsafe Tool Usage: Isolating the execution of tools invoked by agents to prevent misuse or compromise. + Sandboxing Untrusted Code: Automatically generated or untrusted code is executed in a secure, isolated sandbox (e.g., process, container, or microVM) to mitigate risks like Remote Code Execution (RCE), privilege compromise, and resource overload. This proactive approach allows organizations to leverage the power of agentic AI while defending against associated threats.
7. How does AccuKnox provide visibility into Shadow AI?
AccuKnox addresses the challenge of "Shadow AI" through its core capability of Comprehensive Visibility and Auto-Discovery. By onboarding your cloud accounts, our platform automatically discovers and inventories all AI/ML assets, including models, datasets, and compute infrastructure. This creates a single, unified view of all AI components, bringing potentially unsanctioned or unmonitored resources under the purview of the security team and allowing for the consistent application of governance and security policies.
8. How does AccuKnox help with dealing with privacy issues in AI-SPM?
AccuKnox has multiple built-in features to address data privacy: + PII/PHI Scanning of Datasets: The platform can scan AI/ML datasets to identify and flag the presence of Personally Identifiable Information (PII) or Protected Health Information (PHI). + Prompt and Response Firewalling: Our firewall inspects both the input prompts sent to LLMs and the output responses received from them to detect and block the exposure of PII/PHI in real-time.
Does AccuKnox expose privacy-related aspects to external LLMs (e.g., for AI Co-Pilot)?
No, protecting customer data is paramount. AccuKnox implements strict guardrails: + Data Sanitization: We ensure that no telemetry, alert data, or other information containing potential PII/PHI is ever sent to external LLMs for analysis or remediation suggestions. + Tenant-Level Control: The AI-assisted remediation feature can be completely disabled on a per-tenant basis, giving customers full control over whether any of their data interacts with an LLM.
Which platforms and environments does AccuKnox AI security support?
AccuKnox AI security supports a wide range of platforms and environments, including:
AI Governance & Compliance¶
How do you build a defensible AI governance framework aligned to the EU AI Act?
- Start with AI asset discovery & classification (models, datasets, pipelines) mapped to EU AI Act risk tiers (minimal, limited, high-risk).
- Implement policy-as-code governance to enforce controls (data usage, model behavior, access, logging) across the AI lifecycle.
- Enable continuous monitoring for drift, bias, prompt risks, and anomalous usage (LLM security + AI-SPM).
- Maintain auditability & traceability (AIBOM, lineage, decision logs) to demonstrate compliance.
- Integrate automated red teaming to proactively assess safety and regulatory adherence.
How do you define scope for an ISO 42001 compliance journey?
- Identify AI systems in scope (LLMs, ML models, copilots, AI APIs) and their business criticality.
- Map organizational context & stakeholders (data owners, model owners, security, compliance teams).
- Define risk management boundaries (data sources, training pipelines, inference environments).
- Align controls with ISO 42001 domains: governance, risk, lifecycle management, monitoring, and incident response.
- Establish measurable controls & evidence collection (logs, policies, evaluations) for audit readiness.
Can organizations build a custom governance model on top of the platform?
Yes, AccuKnox supports custom policy frameworks using policy-as-code for flexible governance. + Organizations can tailor controls for multi-cloud, on-prem, and hybrid AI environments. + Supports custom risk scoring, compliance mappings (EU AI Act, ISO 42001, NIST AI RMF). + Enables integration with existing security stacks (SIEM, IAM, DevSecOps pipelines). + Provides extensibility via APIs and plugins for organization-specific workflows and guardrails.
AI Attack Detection & Response¶
Could this tool have detected and correlated a multi-layer attack chain like the McKinsey chatbot incident?
Yes, AccuKnox provides end-to-end AI attack surface visibility across prompts, models, APIs, and infrastructure. + Correlates signals from prompt inputs, model behavior, API calls, and runtime anomalies to detect chained attacks. + Uses policy enforcement to flag prompt injection, data exfiltration, and misuse patterns. + Supports cross-layer correlation (AI-SPM + runtime + API security) to reconstruct multi-stage attack paths. + Automated red teaming + continuous validation helps preempt such attack chains proactively.
In the event an AI agent conducts an attack, does the tool provide a process tree or infection chain mapping?
Yes, AccuKnox provides runtime observability with process-level visibility for AI workloads. + Generates process trees and execution lineage across containers, agents, and AI pipelines. + Maps relationships between prompt → model → tool/API call → system action. + Enables forensic analysis with audit trails, logs, and behavior timelines. + Integrates with cloud-native runtime security (eBPF-based) to trace and contain malicious execution paths.
Can it detect supply chain attacks in AI dependencies (e.g., LiteLLM-style compromises)?
Yes, AccuKnox offers AI-SBOM/AIBOM-based visibility into models, libraries, and dependencies. + Continuously scans for vulnerabilities, malicious packages, and integrity violations in AI/ML stacks. + Detects anomalous behavior from compromised dependencies (e.g., unexpected outbound calls, data access). + Enforces trusted registries, signed artifacts, and policy controls for secure model/package usage. + Covers LLM frameworks (e.g., LiteLLM, LangChain) and third-party APIs within the governance scope.
AI Identity & Access¶
Do you identify granular access permissions granted by AI platform licensing models (e.g., Teams with M365 access)?
Yes, AccuKnox provides visibility into identity, roles, and entitlements across AI-integrated platforms (e.g., M365, copilots, and other AI tools). + Maps license-based access (e.g., Teams, Copilot) to actual data and action permissions, identifying over-privileged users and risky entitlements. + Continuously analyzes who can access which datasets, prompts, plugins, and APIs, including lateral access paths. + Enforces least-privilege policies and entitlement governance (AI-KIEM) to reduce misuse and data exposure risks.
How do you assess the intent and access scope of a non-human identity (NHI)?
AccuKnox uses a sandboxing approach to understand AI agent application behaviour at runtime. + Analyzes behavioral patterns and action context to infer intent and detect anomalies. + Evaluates effective permissions vs. required permissions to identify overreach. + Correlates prompt inputs, decisions, and executed actions for explainability. + Enforces least privilege and just-in-time access controls for NHIs.
Shadow AI & Unsanctioned Tools¶
How do you discover unsanctioned AI tools and services across the enterprise?
AccuKnox performs continuous AI asset discovery across endpoints, browsers, SaaS, and cloud environments. + Detects shadow AI usage by analyzing outbound traffic, API calls, and browser interactions with AI services. + Leverages AI-SPM, network telemetry, and browser extensions to identify unauthorized tools and LLM access. + Correlates usage with user identity and data access patterns to assess risk. + Provides policy enforcement to block/alert on unapproved AI services.
Can you detect AI integrations embedded in commercial tools that security teams haven't approved?
Yes, AccuKnox identifies embedded AI capabilities within SaaS platforms (e.g., copilots, plugins, third-party integrations). + Analyzes application behavior, API calls, and data flows to uncover hidden AI usage via AI Detection and Response (AIDR). + Maps data exposure paths from enterprise systems into these embedded AI features. + Flags unauthorized or risky integrations based on governance policies. + Extends visibility into plugin ecosystems and external AI connectors.
Do you integrate with proxy tools to surface AI usage?
Yes, AccuKnox can correlate proxy data with other telemetry sources to surface AI usage across the enterprise. + Correlates proxy data with identity, endpoint, and AI workload telemetry for unified visibility. + Enables real-time policy enforcement (block, allow, monitor) for AI services. + Supports centralized dashboards and reporting for enterprise-wide AI usage monitoring.
Agentic AI Security¶
How do you bring visibility and governance to internally built AI agents?
AccuKnox is purpose-built for securing internally developed AI agents — providing unmatched depth of visibility and control across the full agent lifecycle. + Automatically discovers the full inventory of internally developed agents, models, and pipelines across all environments. + Maps agent capabilities, data access, and tool integrations for complete contextual visibility. + Applies policy-as-code governance across the agent lifecycle (build, deploy, runtime). + Enables continuous monitoring for behavior, drift, and anomalous actions. + Maintains audit trails and lineage (AIBOM) for accountability and compliance.
How do you monitor and apply centralized policies to agentic AI workflows?
AccuKnox provides a comprehensive prompt firewall with capabilities across 12+ categories. Prompt firewall rules can be applied globally across all models and agents and customized with business-logic-specific guardrails. + Enforces guardrails at prompt, model, API, and runtime layers. + Uses real-time policy engines to validate actions before execution (preventive control). + Monitors end-to-end workflows (agent → tools → APIs → data access) for violations. + Supports cross-environment enforcement (multi-cloud, SaaS, on-prem). + Provides continuous validation and red teaming for evolving agent behaviors.
How should enterprises approach vendor risk assessments when adopting third-party agentic AI tools?
- Assess data handling practices, model behavior, and access controls of the vendor.
- Require transparency (AIBOM/SBOM), audit logs, and compliance mappings (EU AI Act, ISO 42001, ISO 27001).
- Validate security posture via red teaming, vulnerability scans, and policy checks.
- Evaluate integration risks (APIs, plugins, data flow into enterprise systems).
- Continuously monitor runtime behavior and third-party access post-deployment.
Data Protection & Prompt Security¶
Does it detect and block sensitive data in prompts - PII, API keys, credentials?
Yes — AccuKnox detects PII, API keys, credentials, and other sensitive data in both prompts and model responses. + Covers data in transit to LLMs as well as generated outputs. + Supports configurable actions such as monitor, alert, or block. + Uses pattern matching + contextual classification for improved detection accuracy. + Helps prevent data leakage and compliance violations in real time.
How does the tool handle false positives — for example, a blocked keyword used in a non-sensitive context?
AccuKnox supports distinct policy types — e.g., regex/keyword-based vs. sensitive data classification. + If a strict keyword policy is configured, it will be enforced regardless of context. + For more contextual accuracy, users can leverage sensitive data detection policies instead of static keyword rules. + This separation allows fine-tuned control based on application use case (precision vs. strict enforcement). + Overall, the platform enables flexible policy design to balance false positives and security requirements.
Does it capture prompts and responses from CLI-based agents like OpenCode or Specit?
Support for CLI-based agent prompt/response capture is currently under development. + Planned support includes visibility into CLI-based agent interactions and prompt/response capture. + Will extend existing coverage beyond browser and API layers into developer tooling environments.
What is the USP versus an existing DLP solution that covers multiple channels?
AccuKnox provides modular, reusable policy components rather than tightly coupled guardrails. + Enables granular control, where each policy is designed for a specific detection task (PII, prompt injection, regex, etc.). + Supports policy reusability across applications, reducing duplication and operational overhead. + Offers better maneuverability and customization, allowing users to optimize for latency vs. security. + Unlike monolithic guardrails (e.g., in AWS Bedrock), policies are decoupled and composable, making changes more efficient and scalable.