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Governança e confiabilidade de IA

As políticas, práticas de avaliação, controles e monitoramento que permitem que sistemas de IA operem de forma responsável, alinhados ao NIST AI RMF, à ISO/IEC 42001 e à regulamentação emergente.

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Visão geral

As AI moves from pilots into decisions that affect customers, employees and finances, leadership needs a credible answer to a basic question: how do we know these systems are working as intended? Governance provides the structure for that answer, and reliability engineering provides the evidence.

We help organizations build both. That includes inventories, risk classification and policy, but also the practical machinery behind them: evaluation pipelines, guardrails, monitoring, incident response and documentation that holds up to internal audit and external scrutiny.

Aplicações de negócio

  1. 01

    AI inventory and risk classification

    Catalog the models, vendors and AI features in use across the organization, including AI embedded in third-party software, and classify each by impact and regulatory exposure to focus oversight where it matters most.

  2. 02

    Evaluation and red teaming

    Establish systematic testing for accuracy, robustness, bias, prompt injection and data leakage, combining automated evaluation pipelines with structured adversarial testing before release and at regular intervals afterward.

  3. 03

    Production monitoring and incident response

    Monitor model quality, drift, safety events and user feedback in live systems, with defined thresholds, on-call ownership and incident procedures for when AI behavior falls outside acceptable bounds.

  4. 04

    Regulatory readiness

    Map AI systems against frameworks and regulations such as the NIST AI RMF, ISO/IEC 42001 and the EU AI Act, and produce the technical documentation those regimes expect from system owners.

Abordagem de engenharia

  1. 01

    Establish the baseline

    Inventory current AI use, existing policies and controls, and compare them against selected frameworks to identify the most significant gaps in oversight and evidence.

  2. 02

    Define policy and accountability

    Set principles, risk tiers, approval requirements and named ownership for each AI system, integrated with existing risk, security, privacy and model risk processes rather than running parallel to them.

  3. 03

    Engineer the controls

    Implement evaluation pipelines, guardrails, access controls, logging and model registries in the delivery toolchain, so compliance evidence is produced by the system rather than assembled by hand.

  4. 04

    Monitor in production

    Deploy observability for quality, drift, safety and cost, with alerting tied to documented thresholds and incident runbooks that name who acts and how.

  5. 05

    Review and adapt

    Run periodic reviews of system performance, incidents and regulatory developments, and update risk assessments, tests and policies as models, uses and obligations change.

Tecnologias e plataformas

Frameworks and standards

  • NIST AI Risk Management Framework
  • ISO/IEC 42001
  • ISO/IEC 23894
  • EU AI Act
  • OWASP Top 10 for LLM Applications
  • MITRE ATLAS

Evaluation and testing

  • promptfoo
  • garak
  • Microsoft PyRIT
  • Ragas
  • Fairlearn

Guardrails and data protection

  • NVIDIA NeMo Guardrails
  • Guardrails AI
  • Microsoft Presidio
  • Llama Guard

Lifecycle and monitoring

  • MLflow Model Registry
  • Evidently AI
  • Arize AI
  • OpenTelemetry
  • Langfuse

Os nomes de tecnologias são marcas registradas de seus respectivos proprietários e não implicam parceria ou endosso.

O que você recebe

  • AI system inventory with risk classification
  • Governance policy, risk tiers and accountability model
  • Gap assessment against the NIST AI RMF and ISO/IEC 42001
  • Automated evaluation and red-teaming pipelines
  • Production monitoring, alerting and AI incident response runbooks
  • Model card and technical documentation templates

Perguntas que os líderes fazem

Can you certify us against ISO/IEC 42001?

No. Certification is performed by accredited certification bodies. We help you design and implement the management system, controls and evidence the standard describes, and prepare for an external audit. Whether and when to pursue certification is your decision, made with the certification body you select.

Is this legal advice on the EU AI Act?

No. We provide engineering and governance support: classifying systems, implementing technical controls and producing documentation. Interpreting legal obligations for your specific situation requires qualified counsel, and we work alongside your legal team so that technical measures reflect their conclusions.

We use AI mostly through vendors. Does governance still apply?

Yes. Much enterprise AI arrives embedded in third-party software. Governance covers how those features are assessed, configured and monitored, what contractual and data protections apply, and how vendor model changes are tracked. We help you extend procurement and third-party risk processes to address AI specifically.

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