Day 1
Useful theory, practical exercises and secure development reflexes: inputs, outputs, sessions, authorization, secrets and error handling.
Book a meeting Practical secure development training, with a 12-month AI pedagogical assistant available after the course.
The training helps developers integrate security from specifications to deployment, not only memorize a vulnerability list.
Participants manipulate weaknesses, understand their impact and learn to correct them properly through practical exercises and a CTF-style consolidation phase.
The objective is to build reflexes that remain useful in real projects: design choices, code reviews, dependencies, deployment and communication with security stakeholders.
The course remains operational: examples, exercises and debriefs are connected to delivery pressure, legacy constraints, team practices and the need to make secure choices without blocking every project discussion.
It can also highlight recurring mistakes already observed during audits so the training speaks directly to the team’s real risk patterns.
Useful theory, practical exercises and secure development reflexes: inputs, outputs, sessions, authorization, secrets and error handling.
CTF format to consolidate learning: search, exploit, understand and correct vulnerabilities in realistic scenarios.
The course speaks to code, architecture, dependencies, reviews, deadlines and delivery constraints.
Limit trust, identify sensitive flows, anticipate abuse cases and avoid insecure defaults.
Validate inputs, encode outputs, protect secrets, handle errors and keep dependencies under control.
Ask better questions during pull requests and technical design discussions.
A pedagogical AI assistant can help learners apply the notions to real projects over time, adapted to the client’s technologies and grounded in reliable secure-development sources.
Understand concrete risks without turning the course into abstract compliance.
Improve review habits and architectural decisions.
Better anticipate security constraints before they become late blockers.
The program can be adjusted according to the stack, maturity, recurring vulnerabilities and the type of applications developed by the team.
Participants do not only exploit weaknesses; they also discuss why the code was vulnerable and which correction is robust enough in practice.
The 12-month AI pedagogical assistant helps learners revisit concepts, ask implementation questions and keep a security reflex during real delivery work.
The content can be converted into review questions for pull requests, architecture discussions and acceptance criteria before a feature reaches production.
Developers, product owners and security stakeholders gain a common vocabulary to discuss trade-offs without reducing security to late blocking comments.
A source code audit or web penetration test can later verify whether the team’s practices are improving on concrete applications.