Challenge

AreYouFacingTheseChallenges?

MultipleModelsinUse,anUncontrollableAICostBlackHole

Enterprises often use multiple AI models at once, with each department paying and consuming independently, causing costs to balloon with no unified tracking mechanism. Finance cannot reconcile spending, and leadership has no visibility into the actual return or distribution of AI investment.

NoAccessControl,SameAIPermissionsforEveryone

All employees use the same AI tools and features, regardless of role or responsibility. Sensitive models and high-privilege agents remain open to everyone, creating a constant risk of improper access and data misuse with no controls in place to prevent it.

UncontrollableAIOutput,UnpredictableComplianceRisk

Generative AI can produce inappropriate content, leak prompt configurations, or unintentionally violate regulations. Without a systematic AI Guardrails mechanism, enterprises can neither block problematic output in advance nor maintain a complete record for later review once a compliance dispute arises.

ShadowAIOutofControl,DataLeaksGoUnnoticed

Employees using public AI tools like ChatGPT and Claude without authorization is now common, with confidential company information, customer data, and internal documents unintentionally flowing into cloud-based models. IT and security teams have no visibility into this until a data leak has already occurred, often too late to act.

Why choose our Solutions

WhyYouNeedAIGovernance?

01

AIAdoptionIsOutpacingGovernanceCapability

Generative AI tools are being adopted far faster than enterprises can build controls for them. While employees already rely heavily on AI in their daily work, many enterprises still lack unified usage policies and oversight frameworks, and every day without governance adds to the accumulating risk.

02

TighteningRegulations,MountingCompliancePressure

The EU AI Act, Taiwan's cybersecurity regulations, and industry compliance requirements continue to tighten, increasing enterprises' responsibility to record, audit, and control AI usage risk. Enterprises without systematic governance face the double impact of regulatory penalties and reputational risk.

03

UncontrolledAICosts,InvisibleReturnonInvestment

Enterprise AI usage keeps growing, yet most organizations have no real grasp of actual Token consumption, model costs, or departmental allocation. Without visibility tools, AI budgets can only be roughly estimated, ROI cannot be verified, and financial decisions lack a solid basis.

04

DataLeakRiskQuietlyBuildingfromWithin

The biggest AI security threat to enterprises is often not an external attack but employees unintentionally sending confidential data or customer information into public models while using AI tools. Without Guardrails and access control, data leaks happen quietly from the inside.

Born from security, Built for trusted

SecuritybyDesign,BuildingTrustworthyAI

SecurityThinkingBuiltIn

Across AIG is developed by Twister5, a company built on a cybersecurity foundation, with governance architecture designed around security from day one. RBAC access control, virtual key mechanisms, and dual-layer Guardrails are not compliance features bolted on after the fact, but core infrastructure of the platform. From the moment an enterprise starts using AI, governance is already running in parallel.

ZeroTrustVerification,SecureAccess

Across AIG integrates GoTrust's Zero Trust identity verification solution, requiring strict identity verification for every AI feature access, ensuring only authorized personnel can operate the corresponding AI resources and models. Identity security doesn't depend on password discipline; it's enforced by technical mechanisms, establishing a trustworthy AI usage environment from the point of access.

TransparentandVisualizedData

A real-time visual dashboard shows Token usage, cost distribution, and request history for every user, department, and model, giving leadership a complete view of AI usage behavior. Trust doesn't come from promises; it comes from data transparency. Governance only becomes real when an enterprise can clearly see the full story behind every instance of AI usage.

Our solution

EnterpriseAIGovernanceandCostControlSolution

01

AIUsageVisibilityDashboard

Integrates AI usage across the enterprise into a real-time visual dashboard showing Token usage, cost distribution, and daily request counts for every user, department, and model, along with budget consumption progress and alert mechanisms. Leadership can grasp the enterprise's overall AI usage without checking in with each department, enabling precise resource allocation decisions.

02

RBAC(Role-BasedAccessControl)

Manages each user's access to AI models, features, and agents based on role and privilege level. Supports role inheritance and cross-tenant isolation, ensuring sensitive models and high-privilege features remain restricted to authorized personnel. Combined with a virtual key mechanism that replaces the distribution of real API keys, keys can be revoked instantly even if exposed, addressing unauthorized access and data misuse at the source.

03

Multi-LayerAIGuardrailsProtection

Establishes a dual-layer AI and security Guardrails mechanism, inspecting both input (prompts) and output (completions) in real time. The AI layer uses semantic analysis to detect malicious prompt injection and data exfiltration attempts, while the security layer uses rule-based filtering to ensure output complies with enterprise policy and regulations. Supports both pre-call interception and post-call review, keeping every AI interaction within safe boundaries.

04

AICostControlandConsolidatedBilling

Precisely manages AI usage and resource allocation for every user and team, supporting budget cap settings and overage alerts to prevent resource misuse and cost overruns. Also provides consolidated billing and invoicing for AI expenses, unifying costs across multiple models such as GPT, Claude, and Gemini, simplifying financial processes so CFOs can clearly track and reconcile every AI expense with ease.

05

HybridCloudAIGovernanceArchitecture

Supports flexible switching between on-premises and cloud-based models, with a unified API interface coordinating resource scheduling across multiple models and intelligent AI load balancing for automatic load distribution and rate control. Whether an enterprise chooses public cloud models or on-premises deployment, everything is managed under a single governance framework, ensuring data sovereignty and compliance requirements are consistently met across hybrid environments.