Technical · Program, outcomes and prerequisites

Secure AI SDLC

Practical corporate course for applying AI to secure AI software lifecycle, with exercises on realistic work, reusable materials and clear governance criteria.

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The problem it solves

The problem it solves

Companies often approach secure AI software lifecycle through scattered experiments: a few prompts, a few enthusiastic users, many doubts about data, quality and responsibility. This course turns that uncertainty into an operating method. Participants work on realistic scenarios, learn where AI helps, where human review remains essential and how to make the practice repeatable inside the company.

Duration
4-6 hours, customisable
Mode
In-person or online lab, with guided exercises and materials adapted to the client.
Profile
For technical teams with programming and software architecture basics.
Final output
Operating canvas for secure AI software lifecycle.

Audience

Audience

For technical teams with programming and software architecture basics.

When to choose it

When to choose it

Choose this course when the company wants concrete progress on secure AI software lifecycle and needs training that produces usable workflows, not abstract theory.

Concrete outcomes

Concrete outcomes

  • Map the work and the decisions where AI can reduce friction.
  • Build practical instructions, checklists and review criteria.
  • Identify data, privacy and responsibility boundaries.
  • Create reusable examples for the team.
  • Define next steps for adoption and governance.

Program

Program

  1. 1
    Architecture and requirements

    Goals, boundaries, data, services and risk assumptions.

  2. 2
    Build and integration patterns

    Pipelines, interfaces, context, permissions and testing.

  3. 3
    Evaluation and quality

    Metrics, review, regression tests and failure modes.

  4. 4
    Production and governance

    Monitoring, security, audit, cost and maintenance.

Practical exercises

Practical exercises

  • Map a realistic process connected to secure AI software lifecycle.
  • Create AI-assisted outputs and review them critically.
  • Define escalation and human review points.
  • Build a reusable checklist for daily work.

Materials delivered

Materials delivered

  • Operating canvas for secure AI software lifecycle.
  • Prompt and instruction templates.
  • Quality and privacy checklist.
  • Risk/control matrix.
  • Adoption notes for the team.

Data, privacy and limits

Data, privacy and limits

The course uses synthetic, public, anonymised or client-approved materials. It explains how to minimise data exposure, protect confidential information, verify outputs and keep human responsibility explicit.

Prerequisites

Basic technical familiarity with software, data or system architecture is recommended.

FAQ

FAQ

Is the course tool-specific?

No. Patterns and workflows are adapted to the tools and policies chosen with the client.

Can company data be used?

Only when accounts, contracts and internal policies allow it. Otherwise synthetic or anonymised data is used.

What remains after the course?

Reusable materials, examples, checklists and a clear set of next steps.

Is it theoretical?

No. The course is built around practical exercises and decisions close to real work.

Contact

A short conversation is enough to understand where to start.

The first 30-45 minute call clarifies the process, goal, available data, constraints and next useful step: training, consulting, data analysis or a controlled technical prototype.

Write to dtr@ar-tik.com