Timia

Timia
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Data Governance & Risk Intelligence

We strengthen the autonomous protection of your digital environments with automations that organize, monitor, and safeguard sensitive information.

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WHAT WE SOLVE

Safe and controlled use of information

We ensure ongoing regulatory compliance and protect data assets, eliminating the risk of penalties and security breaches that damage corporate reputation. We accelerate the discovery and self-service process so that companies can create their own data products autonomously.

HOW?

We automate the cataloging, monitoring, and protection of data using AI detects risk patterns, dynamically applies access policies, and generates real-time compliance reports.

WHAT TECHNOLOGY WE USE

Compatible with multiple tools

At TIMIA adapt to your technological environments and work with the tools your company already uses. Our team is proficient in leading technologies such as Collibra, Alation, Privacera, Microsoft Purview, AWS Macie, Apache Ranger, DataHub, and open source tools for data quality.

Collibra
Alation
Privacy
Microsoft Purview
AWS Macie
Apache Ranger
DataHub
Collibra
Alation
Privacy
Microsoft Purview
AWS Macie
Apache Ranger
DataHub

FAQs

Get answers about Data Governance & Risk Intelligence

Governance ensures that AI used in a responsible, secure, and regulation-compliant manner. responsible, secure, and aligned with regulations. At TIMIA frameworks that cover:

  • privacy and data protection
  • model traceability and auditing
  • bias and explainability checks
  • policies for the safe use of GenAI and LLMs

This allows organizations to move forward with AI exposing themselves to legal, operational, or reputational risks.

We adapt each project to local and international regulations ( data protection, model auditing, security, digital ethics). We integrate controls from the design stage:

  • continuous monitoring
  • access policies
  • automated documentation
  • pre-deployment validations

The goal is for AI scalable and compliant, avoiding penalties and future risks.

Technical debt in AI when models, data pipelines are built without adequate standards, documentation, or MLOps. TIMIA reduce it by:

  • standardization of the model life cycle
  • automation with MLOps/LLMOps
  • data cleansing and versioning
  • modernization of infrastructure and pipelines

This way, we ensure that each new solution is built on solid foundations, not fragile technology.

The adoption of AI business processes, roles, and decisions. That is why we support you with:

  • adoption plans
  • team training
  • updating internal processes and policies
  • definition of responsibilities and new workflows

Structured change management ensures that users adopt and trust in AI.

At TIMIA a progressive approach to reduce uncertainty and accelerate value:

  1. Diagnosis and validation of technical and regulatory risks.
  2. Controlled pilots to assess impact and adjust models.
  3. Progressive scaling with active governance controls.
  4. Full deployment with continuous monitoring.

This model prevents operational disruptions and guarantees reliable, measurable results.

  • Less exposure to failures and vulnerabilities
  • Guaranteed regulatory compliance
  • More sustainable and easier to scale projects
  • Fewer costs resulting from reprocessing or incidents
  • Greater internal confidence in the use of AI

Risk mitigation AI secure, reliable capability secure, reliable, and ready to grow.