Threat Modeling in Data Science Roadmap 2025
Threat Modeling in Data Science Roadmap 2025: Skills You Can’t Ignore
Data science isn’t just about insights anymore — it’s about building secure, resilient, and trustworthy AI/ML systems. Here’s the 2025 skill roadmap every data scientist must master.
Roadmap Skills
- Foundations → Cybersecurity basics, Data governance (GDPR, HIPAA, DPDP), Secure SDLC & STRIDE.
- Data Security in ML → Encryption, RBAC/ABAC, Secure sharing (federated learning, homomorphic encryption).
- Threat Modeling for ML Models → Adversarial ML attacks, Model governance, AI red teaming.
- DevSecOps for Data Science → Secure CI/CD, MLOps security (Kubeflow, MLflow), Cloud-native security (AWS, Azure, GCP).
- Advanced Skills 2025 → AI threat intelligence, Privacy-preserving ML, Zero-Trust, Quantum-resistant security.
Why it matters
By 2025, AI/ML security is a career-defining skill. Threat modeling is no longer optional — it’s a must-have to protect data, models, and businesses. Organizations that invest early in secure ML pipelines will lead in trust, compliance, and resilience.
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