AI Data Security & DLP Analyst — Remote, Freelance

Alignerr Corp.; Municipio de Rincón de los Sauces; Remote; ARS 1,653,000 - 3,582,000; Part time

Benefits offered by this job

Fully remote

Job summary

Alignerr is seeking a Data Security & DLP Analyst (AI Training) to help train and evaluate AI systems on how sensitive information is exposed, detected, and protected. This remote freelance role offers flexible hours and direct impact on cutting-edge security research with top AI labs.

You will analyze real-world data security scenarios, classify risk, and assess prevention and incident response strategies while contributing to AI models' trustworthiness and accuracy.

Qualifications

• 2+ years of experience in data security, compliance, information security, or security operations.
• Familiarity with DLP tools and data classification frameworks; knowledge of privacy or regulatory concepts (GDPR, HIPAA, CCPA).
• Understanding of how data risk manifests in enterprise and cloud environments.
• Ability to work independently and deliver structured, high-quality analysis.
• Curiosity about AI and trustworthy AI development.

Responsibilities

• Analyze data security and DLP scenarios across cloud, SaaS, and enterprise environments.
• Classify data sensitivity levels, exposure pathways, and policy violations.
• Evaluate prevention, detection, and incident response strategies for accuracy and completeness.
• Generate, label, and validate data security cases for training and benchmarking AI models.
• Help AI systems develop accurate reasoning about sensitive information risk.

Skills

Data security

Compliance

Information security

Security operations

Tools

DLP tools

Data classification frameworks

Job description

Alignerr is seeking a Data Security & DLP Analyst (AI Training) to help train and evaluate AI systems on how sensitive information is exposed, detected, and protected. This remote freelance role offers flexible hours and direct impact on cutting-edge security research with top AI labs.

You will analyze real-world data security scenarios, classify risk, and assess prevention and incident response strategies while contributing to AI models' trustworthiness and accuracy.