AI Security Governance

Responsible AI, Built on Trust

Managing invisible AI risks to build a trustworthy AI operating environment

Securing the Future of AI

AI Security Governance validates security risks across AI models and services, establishing the foundation of trust that sustainable AI adoption requires.

Market Needs & Client Challenges

AI Adoption Is
Outpacing Control

Spreading AI Adoption, Lagging Governance

Unmapped AI Adoption
Across the Organization

An organization's AI usage and the scope of its internal data connections can fall into management blind spots.
Uncontrolled AI connections lead to data leaks, malfunctions, and the execution of unauthorized tasks.

Shadow Usage Leaves Compliance Blind Spots

If unauthorized AI use and policy compliance are not made visible, gaps arise in privacy protection and audit response.
Shadow AI increases the risks of sensitive information leaks and data misuse.

AI-specific Threats Beyond Traditional Controls

AI-specific threats such as prompt injection and agent privilege abuse fall outside the scope of traditional security assessments.
Model jailbreaking, sensitive information exposure, and data poisoning call for a separate AI security verification framework.

Solution Overview & Benefits

From AI Risk to Responsible Operations

Turning AI Risk into Responsible Operations

Why AI Security Governance Matters

AI security governance is a framework that comprehensively manages risks across AI models, data, privileges, external integrations, and operational policies. S2W identifies management blind spots in AI environments and helps turn security verification results into operational policies and controls.

Map AI Risks, Establish Controls

By analyzing an organization's AI models, data flows, user privileges, and the integration structure of external APIs and agents, it identifies potential risks and management blind spots. Based on this, it establishes control criteria and response priorities for each risk.

AI Asset IdentificationAI Data Flow ManagementAI Privilege Management

Validate AI Controls, Reduce Misuse Risk

Based on AI-specific threats such as prompt injection, model jailbreaking, sensitive information elicitation, and unauthorized task execution, it examines the effectiveness of policies, privilege controls, and data protection safeguards. For areas requiring technical verification, it links with AI red teaming to pinpoint vulnerabilities and improvement tasks.

AI Safeguard VerificationPrompt Injection ResponseAI Misuse Risk Management

Operationalize Governance, Sustain Compliance

Establishing AI usage policies, data handling standards, access privilege controls, log auditing, and incident response procedures tailored to the organizational environment. This lowers compliance risk and builds a trustworthy AI usage environment and security guardrails.

AI Operations PolicyAI Audit FrameworkShadow AI Management

Proven Expertise & Operational Excellence

Security-tested Governance,
Built for Operations

AI Governance That Turns Validation into Control

End-to-End AI Risk Governance

AI governance is not completed through policy documents alone. S2W examines prompts, models, data, external tools, and agent privileges from the perspective of real attack and misuse scenarios, and reflects the results in control criteria and operational policies.

Integrated Risk Assessment
Analysis across models, data, services, agents, and privileges
Verification based on AI-specific attack scenarios
Reflecting real-world experience in security assessment of generative AI services
Governance Design
Connecting technical security verification with operational policy

From Findings to Actionable Controls

S2W does not leave the discovered vulnerabilities and operational gaps as a mere report. Taking into account the organization's level of AI adoption and operating environment, it turns them into improvement priorities, policy criteria, roles and responsibilities, an audit and monitoring framework, and a phased implementation roadmap.

Deriving Improvement Tasks and Response Criteria
Turning assessment results into execution priorities
Designing an operational control framework
Connecting policy, privileges, auditing, monitoring, and incident response
Phased Improvement Roadmap
Reflecting the organization's level of AI adoption and operating environment

End-to-End AI Risk Governance

AI governance is not completed through policy documents alone. S2W examines prompts, models, data, external tools, and agent privileges from the perspective of real attack and misuse scenarios, and reflects the results in control criteria and operational policies.

Integrated Risk Assessment
Analysis across models, data, services, agents, and privileges
Verification based on AI-specific attack scenarios
Reflecting real-world experience in security assessment of generative AI services
Governance Design
Connecting technical security verification with operational policy

From Findings to Actionable Controls

S2W does not leave the discovered vulnerabilities and operational gaps as a mere report. Taking into account the organization's level of AI adoption and operating environment, it turns them into improvement priorities, policy criteria, roles and responsibilities, an audit and monitoring framework, and a phased implementation roadmap.

Deriving Improvement Tasks and Response Criteria
Turning assessment results into execution priorities
Designing an operational control framework
Connecting policy, privileges, auditing, monitoring, and incident response
Phased Improvement Roadmap
Reflecting the organization's level of AI adoption and operating environment