MLSecOps
What is MLSecOps?
MLSecOpsThe discipline of integrating security and risk controls across the entire machine-learning lifecycle, from data sourcing through training, deployment, monitoring, and retirement.
MLSecOps extends DevSecOps to machine learning. It treats data, code, models, prompts, and inference infrastructure as first-class assets that need provenance, signing, vulnerability management, and continuous testing. Programs typically cover dataset governance, training-time integrity (against poisoning and backdoors), supply-chain controls for open-source models and dependencies, secure model registries, runtime monitoring for drift and abuse, red teaming, and incident response. Frameworks such as NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, and the OWASP ML/LLM Top 10 give MLSecOps teams a shared taxonomy. Mature programs ship AI Bills of Materials, automate evaluation gates in CI/CD, and align with broader product-security and privacy obligations.
● Examples
- 01
A CI/CD pipeline that blocks model deployment if adversarial-evaluation or bias scores regress beyond a threshold.
- 02
A central registry that records dataset hashes, training configs, and red-team results for every production model.
● Frequently asked questions
What is MLSecOps?
The discipline of integrating security and risk controls across the entire machine-learning lifecycle, from data sourcing through training, deployment, monitoring, and retirement. It belongs to the AI & ML Security category of cybersecurity.
What does MLSecOps mean?
The discipline of integrating security and risk controls across the entire machine-learning lifecycle, from data sourcing through training, deployment, monitoring, and retirement.
How does MLSecOps work?
MLSecOps extends DevSecOps to machine learning. It treats data, code, models, prompts, and inference infrastructure as first-class assets that need provenance, signing, vulnerability management, and continuous testing. Programs typically cover dataset governance, training-time integrity (against poisoning and backdoors), supply-chain controls for open-source models and dependencies, secure model registries, runtime monitoring for drift and abuse, red teaming, and incident response. Frameworks such as NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, and the OWASP ML/LLM Top 10 give MLSecOps teams a shared taxonomy. Mature programs ship AI Bills of Materials, automate evaluation gates in CI/CD, and align with broader product-security and privacy obligations.
How do you defend against MLSecOps?
Defences for MLSecOps typically combine technical controls and operational practices, as detailed in the full definition above.
What are other names for MLSecOps?
Common alternative names include: ML security operations, AI SecOps.
● Related terms
- ai-security№ 025
AI Bill of Materials (AIBOM)
A machine-readable inventory of every component that goes into an AI system — datasets, base models, fine-tuning data, libraries, prompts, and evaluation artifacts — used for security, compliance, and accountability.
- ai-security№ 034
AI Supply Chain Risk
The set of threats arising from the third-party datasets, base models, libraries, plug-ins, and infrastructure that organisations combine to build and deploy AI systems.
- ai-security№ 032
AI Red Team
A specialised team that simulates adversaries against AI systems to uncover safety, security, and misuse risks before real attackers do.
- ai-security№ 027
AI Governance
The policies, processes, roles, and controls organisations and regulators use to ensure AI systems are developed, deployed, and operated responsibly and lawfully.
- ai-security№ 777
OWASP LLM Top 10
An OWASP-maintained list of the ten most critical security risks affecting applications that build on large language models.
- ai-security№ 029
AI Incident Response
The set of processes, roles, and playbooks an organisation uses to detect, contain, investigate, communicate, and recover from incidents involving AI systems.
● See also
- № 281Data Poisoning
- № 703Model Extraction
- № 704Model Inversion
- № 018Adversarial Example
- № 393Evasion Attack (ML)
- № 081Backdoor Attack (ML)
- № 666Membership Inference Attack
- № 617LLM Firewall