AI Advisory & Governance
Establish strategic direction, accountability, and policy.
- AI governance framework development
- AI policy and guideline development
- AI readiness assessment
- AI strategy and roadmap development
- ESG and AI alignment advisory
Responsible AI from strategy to secure implementation
AI Governance, Risk, Compliance & Security
A comprehensive service portfolio for organizations adopting AI, machine learning, generative AI, LLMs, and agentic AI while managing governance, ethical, regulatory, operational, and cybersecurity risks.
Service Overview
Innovate with confidence, accountability, and security.
Karsa combines governance, risk, compliance, security, capability development, and implementation support so that trust is embedded throughout the AI lifecycle.
Official service portfolio: Karsa AI publicly groups services into AI Advisory and Governance, AI Risk and Security, AI Capability Development, and AI Solution Implementation, including governance frameworks, policies, readiness, strategy, compliance audits, secure lifecycle, red teaming, risk and impact assessments, training, XAI, enterprise integration, and agentic AI implementation.
Scope of Services
What we can deliver.
AI Risk, Compliance & Assurance
Identify, evaluate, treat, and evidence AI risks.
- AI regulatory compliance and audit
- AI risk and impact assessments
- Use-case inventory and risk classification
- Control mapping, evidence, issue, and action management
- Independent governance and control assessment
AI Security & Red Teaming
Test and strengthen AI systems against misuse and attack.
- AI security architecture review
- AI secure lifecycle development
- Threat modeling for AI and LLM systems
- AI red teaming and adversarial testing
- Remediation validation and security improvement
AI Capability Development
Equip decision-makers and delivery teams with practical skills.
- AI awareness and ethics campaigns
- Executive and staff training on AI governance and risk
- AI red teaming and scenario planning exercises
- Curriculum development for AI governance
- Certification-oriented capability programs
AI Solution Implementation
Move responsible AI requirements into real systems.
- End-to-end AI project management
- AI model lifecycle management
- AI security implementation
- Explainable AI implementation
- Enterprise integration and agentic AI implementation
AI Governance Operations
Sustain oversight throughout the AI lifecycle.
- Intake and use-case review workflow
- Approval gates and human oversight
- Model, dataset, prompt, and change governance
- Monitoring, incident, exception, and retirement processes
- Management reporting and continuous improvement
Typical Deliverables
Practical outputs your teams can use.
- AI strategy and responsible AI roadmap
- AI governance framework and committee/role model
- AI policy, standard, guidelines, and acceptable-use requirements
- AI use-case inventory and risk-classification model
- AI readiness, maturity, risk, and impact assessment reports
- AI control catalog and requirements traceability matrix
- AI security architecture review and threat model
- AI red-team plan, test cases, findings, and remediation report
- Secure AI/LLM lifecycle requirements and stage gates
- Dataset, model, prompt, access, logging, monitoring, and change controls
- Human oversight and automated-decision governance requirements
- Explainability, transparency, and evidence requirements
- AI incident, exception, escalation, and model retirement processes
- Training materials, exercises, and capability-development roadmap
Delivery Method
A structured path from risk to control.
Step 01
Discover
Inventory AI use cases, models, data, vendors, users, business objectives, and regulatory context.
Step 02
Classify
Assess materiality and risk based on impact, autonomy, data sensitivity, users, and deployment context.
Step 03
Design
Define governance, roles, policies, controls, architecture, lifecycle gates, and evidence requirements.
Step 04
Test
Perform risk assessment, architecture review, red teaming, control testing, and scenario exercises.
Step 05
Implement
Integrate controls into delivery workflows, platforms, models, applications, monitoring, and human oversight.
Step 06
Sustain
Operate reporting, incidents, changes, exceptions, re-assessments, training, and continuous improvement.
Engagement Options
Select the model that fits your maturity and timeline.
Baseline
AI Readiness & Risk Assessment
Inventory, classify, assess, and prioritize the AI governance and security roadmap.
Build
AI GRC Implementation
Develop and operationalize the governance framework, policies, lifecycle controls, architecture, and workflows.
Assure
AI Security & Red Teaming
Test models, LLM applications, agents, integrations, and governance controls; validate remediation.
Frequently Asked Questions
Common questions.
Scope, duration, and exact artifacts are finalized during initiation based on your organization, systems, and risk profile.
Yes. The official Karsa AI portfolio explicitly includes enterprise AI integration and agentic AI implementation, while security and governance scope can address prompts, tools, memory, data access, actions, logging, monitoring, and human controls.
Yes. Scope can include vendor due diligence, contractual and data risks, model transparency, hosting, access, integration security, output risks, monitoring, and exit requirements.
The exact test plan depends on the system. It may cover prompt injection, jailbreaks, sensitive-data leakage, unsafe outputs, tool misuse, excessive agency, access-control failure, model extraction, data poisoning exposure, and governance process weaknesses.
Yes. The service can translate requirements into intake, classification, approval, lifecycle gates, test evidence, change controls, incident processes, exception handling, dashboards, and management reporting.
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Explore service →Start with a focused consultation.
Describe your objectives, regulatory drivers, systems, and desired timeline. Karsa will propose a fit-for-purpose scope.