Secure and Govern Enterprise AI Across Applications, Agents, and Workflows
Quokka Labs helps enterprises discover employee AI usage, secure LLM and RAG applications, govern agents and MCP environments, and enforce policy through runtime guardrails, continuous monitoring, and audit-ready controls.
Operational Capabilities That Strengthen AI Security and Governance
AI agents, LLMs, MCP servers, and RAG application governance
Prompt security, model validation, and runtime guardrails
Shadow AI discovery, AI inventory, and model governance
AI risk management, policy enforcement, and access governance
AI compliance aligned with ISO/IEC 42001 and the EU AI Act
AI observability, audit trails, and continuous governance assurance
AI Security and Governance Services
Secure and Govern Enterprise AI Across Applications, Agents, and Workflows
Quokka Labs helps enterprises discover employee AI usage, secure LLM and RAG applications, govern agents and MCP environments, and enforce policy through runtime guardrails, continuous monitoring, and audit-ready controls.
Operational Capabilities That Strengthen AI Security and Governance
AI agents, LLMs, MCP servers, and RAG application governance
Prompt security, model validation, and runtime guardrails
Shadow AI discovery, AI inventory, and model governance
AI risk management, policy enforcement, and access governance
AI compliance aligned with ISO/IEC 42001 and the EU AI Act
AI observability, audit trails, and continuous governance assurance
Trusted AI Security & Governance Partner
Trusted by Teams Building Secure and Governed AI Systems
Quokka Labs combines AI engineering, cybersecurity, cloud, data, and governance expertise to help organizations move AI from experimentation to controlled, production-ready adoption.
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Years of Engineering Experience
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Digital, Cloud, and AI Projects Delivered
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Engineers and Technology Experts
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Industry Domains Supported
AI Security & Governance Capabilities
Enterprise Controls for Secure and Accountable AI
Quokka Labs helps organizations secure AI systems, establish governance ownership, validate production behaviour, operationalize policies, and maintain regulatory evidence across models, applications, data, agents, and enterprise AI usage.
AI Security
01
Protect LLM applications, RAG pipelines, copilots, agents, and MCP workflows against prompt injection, jailbreaks, data leakage, insecure retrieval, model misuse, and unauthorized tool execution.
Generative AI Governance
02
Establish AI inventories, ownership models, risk tiers, approval workflows, model and prompt lineage, access controls, and lifecycle accountability across enterprise generative AI initiatives.
Responsible AI Deployment
03
Validate AI systems through model evaluations, fairness and explainability reviews, human-in-the-loop controls, confidence thresholds, fallback behaviour, and production release gates.
AI Compliance & Audit Automation
04
Operationalize control mapping, evidence collection, decision logs, risk documentation, approval records, and audit reporting aligned with applicable AI governance frameworks and regulations.
Multimodal AI Governance
05
Govern text, image, audio, video, and multimodal systems through content-safety controls, data provenance, consent management, modality-specific evaluation, and consistent policy enforcement.
AI Policy Management
06
Define and enforce policies for acceptable AI use, Shadow AI, sensitive data, model access, agent permissions, exceptions, and escalation through structured workflows and policy-as-code controls.
Find the Gaps Between AI Adoption and Enterprise Control
Quokka Labs evaluates your AI inventory, Shadow AI exposure, data flows, model and agent risks, policy coverage, and regulatory readiness to identify control gaps and define a prioritized security and governance roadmap.
Operationalizing AI Security and Governance Across Intelligent Systems
Explore how Quokka Labs helps organizations establish secure AI operating models through structured oversight, policy-driven controls, lifecycle governance, and responsible AI practices across intelligent products, platforms, and business workflows.
Langprotect
Quokka Labs developed an enterprise AI security platform that enables centralized AI governance, real-time policy enforcement, and continuous risk monitoring, helping organizations secure AI systems while maintaining compliance and operational trust.
Quokka Labs developed a mobile VPN security platform with real-time threat detection, phishing protection, and breach monitoring, strengthening privacy, secure connectivity, and governance across digital interactions.
Quokka Labs developed an AI security platform that enabled Zero Trust access through biometric authentication, adaptive MFA, and secure identity orchestration, helping organizations establish trusted access policies and governance across distributed applications.
AI Security and Governance Solutions Built for Industry-Specific AI Systems
Every industry faces distinct AI risks, regulatory obligations, and governance challenges. Quokka Labs delivers AI governance consulting & solutions that align AI oversight, model accountability, and policy controls with industry-specific AI ecosystems and operational requirements.
Standards and Platforms for Secure, Accountable AI
Quokka Labs aligns AI security and governance with recognized frameworks, regulatory requirements, runtime safeguards, model assurance platforms, and enterprise control systems across the AI lifecycle.
ISO/IEC 42001
ISO/IEC 23894
NIST AI RMF
OECD AI Principles
IEEE 7000 Series
ISO/IEC 42001
ISO/IEC 23894
NIST AI RMF
OECD AI Principles
IEEE 7000 Series
OWASP Top 10 for LLM Applications
MITRE ATLAS
CSA AI Controls Matrix
NIST CSF 2.0
OWASP Top 10 for LLM Applications
MITRE ATLAS
CSA AI Controls Matrix
NIST CSF 2.0
EU AI Act
GDPR
CCPA/CPRA
DPDP Act
HIPAA
EU AI Act
GDPR
CCPA/CPRA
DPDP Act
HIPAA
Azure AI Content Safety
AWS Bedrock Guardrails
Google Model Armor
NVIDIA NeMo Guardrails
Azure AI Content Safety
AWS Bedrock Guardrails
Google Model Armor
NVIDIA NeMo Guardrails
IBM watsonx.governance
OneTrust AI Governance
Arize AI
Fiddler AI
WhyLabs
MLflow
IBM watsonx.governance
OneTrust AI Governance
Arize AI
Fiddler AI
WhyLabs
MLflow
LangSmith
Langfuse
Evidently AI
OpenTelemetry
Grafana
Datadog
LangSmith
Langfuse
Evidently AI
OpenTelemetry
Grafana
Datadog
Microsoft Entra ID
Okta
CyberArk
Keycloak
HashiCorp Vault
AWS IAM
Microsoft Entra ID
Okta
CyberArk
Keycloak
HashiCorp Vault
AWS IAM
ServiceNow IRM
OneTrust
AuditBoard
Archer
MetricStream
Microsoft Purview
ServiceNow IRM
OneTrust
AuditBoard
Archer
MetricStream
Microsoft Purview
Engineering-Led AI Governance
Why Enterprises Choose Quokka Labs
Quokka Labs combines AI architecture, cybersecurity, model risk, and governance engineering to turn enterprise policies into enforceable controls across AI adoption, development, deployment, and production operations.
AI Estate Visibility
We discover and map employee AI usage, models, datasets, applications, RAG pipelines, agents, MCP servers, tools, and integrations to establish ownership, risk boundaries, and governance priorities.
Threat-Led Engineering
Our specialists assess trust boundaries and AI-specific attack paths, including prompt injection, insecure retrieval, data leakage, model abuse, excessive agent permissions, tool misuse, and supply-chain exposure.
Policy-to- Runtime
Acceptable-use rules, data controls, approval requirements, model-access policies, and agent permissions are translated into guardrails, workflow gates, and auditable runtime enforcement.
Production Assurance
AI systems are evaluated through red teaming, model and RAG testing, agent validation, output assessment, human-review controls, and continuous monitoring before and after deployment.
Enterprise Integration
Governance controls integrate with IAM, SIEM, cloud platforms, data-security systems, GRC tools, DevSecOps pipelines, and enterprise applications rather than operating as an isolated layer.
Audit-Ready Operations
Decision logs, model and data lineage, control ownership, approval records, policy exceptions, evaluation results, and compliance evidence support continuous accountability and regulatory readiness.
Our governance strategies align with your AI architecture, operational priorities, risk profile, and regulatory obligations.
Technologies Powering Secure, Governed, and Production-Ready AI Systems
We combine modern AI frameworks, cloud platforms, identity systems, observability tools, and secure infrastructure to build scalable, production-ready AI environments with operational resilience and governance built in.
Figma
AI Security & Governance Lifecycle
How We Operationalize AI Security and Governance Across Intelligent Systems
Our AI experts begin with understanding your AI ecosystem before establishing governance controls, operational guardrails, continuous oversight, and measurable accountability that evolve alongside your AI systems and business objectives.
1
AI Asset Discovery
We identify models, datasets, prompts, applications, RAG pipelines, copilots, agents, MCP servers, tools, integrations, and Shadow AI to establish ownership, dependencies, and governance boundaries.
2
Risk Classification
AI systems are classified by business criticality, data sensitivity, autonomy, user impact, regulatory exposure, and potential harm to determine the required level of control and oversight.
3
Threat & Abuse Modeling
We map trust boundaries, data flows, retrieval paths, model interactions, agent permissions, and tool access to identify prompt injection, data leakage, insecure retrieval, model abuse, and unauthorized execution risks.
4
Governance Design
Our specialists define ownership, approval workflows, access policies, human oversight, evaluation criteria, exception handling, risk thresholds, and control mappings across the AI lifecycle.
5
Security Control Implementation
We implement prompt and output controls, retrieval safeguards, identity enforcement, agent authorization, tool restrictions, sensitive-data protection, and policy-based runtime guardrails.
6
Red Teaming & Validation
AI systems undergo jailbreak testing, prompt injection simulations, RAG evaluation, agent abuse testing, model-behaviour assessment, bias review, and control verification before production release.
7
Runtime Monitoring & Optimization
We establish AI observability, policy monitoring, lineage, decision traceability, drift detection, incident workflows, evidence collection, and recurring control reviews as models and regulations evolve.
Perspectives on Building Secure, Governed, and Production-Ready AI
Explore expert insights on AI security, governance, regulatory readiness, operational resilience, and emerging AI risks that shape responsible AI adoption across modern organizations.
AI Security
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Whether you're governing LLMs, AI agents, or intelligent workflows, Quokka Labs’ AI experts help you strengthen AI oversight, operational controls, and regulatory readiness through practical AI governance consulting & solutions tailored to your AI strategy.
Governance Readiness Review
Evaluate governance gaps, AI risks, and operational readiness across your AI ecosystem.
Strategic AI Roadmap
Receive implementation-focused recommendations aligned with your AI priorities and regulatory requirements.
Engineering-Led Delivery
Embed AI security and governance into production systems through experienced AI engineers and governance specialists.
Book your free AI assessment
AI Security and Governance FAQs
Why can't existing security controls fully govern AI systems?
Existing security controls protect infrastructure, applications, and identities but don't address AI-specific challenges such as model governance, prompt security, AI agents, explainability, Shadow AI, or continuous AI assurance.
Why do organizations need AI security and governance alongside cybersecurity?
Cybersecurity protects digital assets and infrastructure, while AI security and governance address AI-specific risks including prompt injection, Shadow AI, model drift, hallucinations, explainability, and governance throughout the AI lifecycle.
What AI systems should be included in an AI governance program?
An effective governance program should include LLMs, AI agents, RAG applications, multimodal AI, machine learning models, intelligent automation, AI copilots, third-party AI platforms, datasets, prompts, and Shadow AI used across the organization.
How do you protect AI applications from prompt injection and other AI-specific threats?
We implement layered controls including prompt validation, runtime monitoring, AI risk assessments, access governance, adversarial testing, policy enforcement, and continuous observability to reduce AI-specific attack surfaces before production deployment.
Can AI governance support compliance with regulations like the EU AI Act and ISO/IEC 42001?
Yes. We align governance practices with globally recognized frameworks including the EU AI Act, ISO/IEC 42001, NIST AI RMF, GDPR, and industry-specific regulatory requirements while embedding governance into day-to-day AI operations.
Which AI governance solution is right for an organization?
The right AI governance solution depends on your AI maturity, regulatory obligations, AI architecture, and operational requirements. Organizations deploying LLMs, AI agents, or RAG systems typically require governance capabilities beyond basic policy management.
What are the most scalable governance solutions for AI and LLMs?
The most scalable governance solutions for AI and LLMs combine model lifecycle management, runtime monitoring, policy orchestration, AI observability, and automated compliance into a unified governance operating model.
How do enterprise AI governance solutions differ from standard AI governance?
Enterprise AI governance solutions provide centralized policy enforcement, model inventories, AI risk management, auditability, and governance controls across multiple business units, AI platforms, and production environments.
What are the best AI governance frameworks for small companies?
The best AI governance frameworks for small companies are those that establish clear policies, role-based accountability, model oversight, and regulatory readiness without introducing unnecessary operational complexity.
When should an organization choose an AI governance company?
Organizations should consider an AI governance company when AI adoption expands across multiple teams, LLMs, AI agents, or automated workflows. As AI systems become more business-critical, governance helps establish policy controls, AI risk management, regulatory readiness, model accountability, and operational oversight that support secure, scalable, and responsible AI adoption.
