ZIRH.AI® helps enterprises adopt AI agents with the controls, visibility and evidence required to protect data, govern actions and scale with confidence.
Six capability layers take AI agents from readiness assessment through identity, access, runtime control and audit-ready evidence — so adoption stays fast and controlled.
Evaluate where AI agents can create value, where they may create risk, and what must be ready before adoption.
Create a clear view of all AI agents across the enterprise and assign accountability before usage scales.
Define who each agent is, what it can access, which actions require approval, and how credentials are handled.
Enable agents to connect with approved systems, APIs, MCP services and enterprise knowledge through controlled access paths.
Coordinate agent, human and system steps while keeping runtime actions within approved operating limits.
Monitor agent behavior, capture runtime activity and maintain structured evidence for security, risk, audit and compliance teams.
ZIRH.AI® addresses both sides of enterprise AI adoption: moving fast enough to stay competitive and controlling risk enough to protect the business.
We help enterprises move from AI hesitation to controlled adoption by combining governance, access, protection and evidence.
The platform focuses on AI agents that access tools, use data, trigger workflows and take actions — not only on static AI models.
Shared platform capabilities power standalone products, so clients start with governance, gateway or protection depending on their maturity.
We balance moving fast enough to stay competitive with controlling risk enough to protect the business.
The platform turns agent ownership, access, actions, incidents and tests into structured evidence for decision-making and assurance.
ZIRH.AI® can be positioned for private, sovereign or on-premise deployment where data sensitivity and operational control are critical.
ZIRH.AI® translates management-system and risk-framework logic into agent-level operating practices — linking every agent to ownership, controls and reviewable evidence.
Defines the requirements for establishing, implementing, maintaining and continually improving an AI management system. ZIRH.AI® translates this into agent-level practice by linking each AI agent to ownership, lifecycle status, risk assessment, usage rules, control records and improvement actions.
Structures AI risk management around Govern, Map, Measure and Manage. ZIRH.AI® aligns by helping enterprises map agent usage and exposure, measure risk and control effectiveness, define governance responsibilities and manage remediation across agent workflows.
Introduces a risk-based framework, with high-risk systems expected to maintain risk management, technical documentation, logging, transparency, human oversight, accuracy, robustness and cybersecurity controls. ZIRH.AI® operationalizes these expectations for AI agents by keeping ownership, access rights, runtime activity, security checks and control evidence structured and reviewable.
Focuses on digital operational resilience for financial entities, including ICT risk management, incident handling, resilience testing and third-party technology risk. ZIRH.AI® is relevant where AI agents interact with financial systems, third-party tools or operational workflows — recording activity, identifying incidents, tracking exceptions and supporting resilience evidence.
Requires data protection by design and by default, limiting personal data processing to what is necessary with appropriate technical and organizational measures. ZIRH.AI® applies these principles to AI agents through purpose-based access boundaries, permission-aware knowledge retrieval, activity records and data leakage controls.
Emphasizes that AI activities involving personal data should be designed and operated in line with Turkish data protection legislation and secondary regulations. ZIRH.AI® addresses this by controlling which agents can access personal data, recording how data is used, reducing leakage risk and maintaining accountable usage evidence.
Book a working session with our team to map where AI agents create value — and the controls that make them safe to scale.