Challenges we solve
Are we building AI on a secure foundation? · Can we scale without compromising compliance or reputation? · How do we justify technical decisions in an audit?
Secure AI Lifecycle
Security at every stage of the AI lifecycle
We support your AI systems from conception to retirement, with active controls and traceable evidence at every stage.
Why now
Scale AI without compromising security
Organizations face a new challenge: how to scale AI without compromising security, traceability or compliance.
Our Secure AI Lifecycle portfolio answers with a modular architecture that supports each AI system from conception to retirement, with sound technical principles, active controls and traceable evidence at every stage.
Five integrated lines
From design to the retirement of every model
- 1Technical strategy and architecture
- 2Secure design and technical risk assessment
- 3Development, validation and traceability
- 4Deployment, monitoring and response
- 5Retirement, retraining and obsolescence
Line 01
Technical strategy and architecture
Lay secure foundations for developing and operating AI, aligned with ISO/IEC 42001, OWASP LLM and NIST CSF, and identify critical assets and structural risks from the outset.
Practices
Zero Trust applied to AI · AI asset inventory and risk classification · Modular architecture with embedded controls
Deliverables
Secure architecture blueprint · Map of assets and algorithmic risks · Technical principles aligned with standards · Regulatory compatibility report
Why Cloud Legion: without a secure, scalable and traceable architecture, any AI system creates unnecessary risk. We build on foundations that stand up to audits.
Line 02
Secure design and technical risk assessment
Build in controls from the design stage, validate datasets and models against specific threats, and meet ethical and regulatory principles.
Challenges we solve
Is our data poisoned or biased? · What if the model behaves unexpectedly? · Are we exposed to reputational or regulatory risk?
Practices
Dataset sanitization and bias audits · Adversarial attack simulation · Ethical and technical model validation
Deliverables
Technical risk report · Algorithmic threat matrix · Dataset and model validation · Mitigation recommendations by risk type
Why Cloud Legion: the biggest AI incidents start at the design stage. We anticipate vulnerabilities before they reach production.
Line 03
Development, validation and traceability
Ensure robustness, auditability and control: full traceability of models and datasets, testing against adverse scenarios and documented decisions.
Challenges we solve
Can we audit how this model was trained? · What if the model fails in production? · Do we have control over versions and updates?
Practices
Secure CI/CD for AI · Technical and documentary traceability · Continuous validation and stress testing
Deliverables
Secure, documented MLOps pipeline · Version and change log · Model Cards with technical and ethical metadata · Technical validation report
Why Cloud Legion: running AI without traceability is like flying without instruments. Every model, dataset and decision is documented, versioned and validated.
Line 04
Deployment, monitoring and response
Run AI with continuous visibility and control, detect and respond to algorithmic anomalies, and integrate it with existing security platforms.
Challenges we solve
How do we know AI is working as it should? · What do we do about an algorithmic incident? · Can we explain every AI decision?
Practices
AI observability and logging · Built-in explainability · Incident response playbooks
Deliverables
Algorithmic monitoring dashboard · Traceable, explainable logs · Algorithmic incident playbook · Performance and anomaly report
Why Cloud Legion: AI in production needs continuous control. We integrate it with your security and observability platforms.
Line 05
Retirement, retraining and obsolescence
Decommission or update models without compromising security or continuity, retrain in a controlled way and preserve technical evidence.
Challenges we solve
What happens to models we no longer use? · How do we keep retraining from introducing new risks? · Do we have evidence to defend past decisions?
Practices
Secure decommissioning and log preservation · Retraining with ethical validation · Post-retirement technical audit
Deliverables
Retirement protocol and checklist · Retraining and validation report · Obsolescence and replacement record · Documentary evidence for audit
Why Cloud Legion: the AI lifecycle doesn't end at deployment. We retire models securely and preserve evidence for audits and legal defense.
Technology we integrate
Partners to protect the AI lifecycle
We choose platforms that cover the AI supply chain, defenses against adversarial attacks and runtime monitoring.
Palo Alto Networks
AI-SPM for visibility into the security posture of your own AI applications, and AI Runtime Security to protect apps and agents in production against prompt injection, exploits and data leakage.
Protect AI
Guardian scans models before deployment, Recon simulates red team attacks and Layer detects and responds to threats in real time.
Pillar Security
AI Discovery to inventory AI assets, AI-SPM, red teaming against the OWASP Top 10 for LLMs, adaptive guardrails and telemetry to monitor and govern.
AI leaders
First Argentine company certified in ISO 42001
The first consultancy in Latin America to sign the CSA AI Trustworthy Pledge, and the region's first success story in ISO 42001 readiness and certification.
FAQ
What clients ask us most
What is Secure AI Lifecycle?
An approach that applies security controls at every stage of an AI system: strategy and architecture, design, development and validation, deployment and monitoring, and retirement or retraining.
What security risks does AI have?
Among others, prompt injection, data poisoning, sensitive data leakage, model theft and biased or untraceable decisions.
Protect your AI end to end
We help you design, run and retire AI systems securely and with full traceability.