Security frameworks fall into three broad categories:
- Governance & risk frameworks (organizational security management)
- Secure development frameworks (secure SDLC / AppSec)
- AI-specific security & governance frameworks (emerging area)
Below are the most widely used frameworks in industry today, especially relevant to software development and AI systems.
Major Governance & Cybersecurity Frameworks
These provide enterprise-level security governance, risk management, and compliance.
1. NIST Cybersecurity Framework (CSF)
- Developed by the National Institute of Standards and Technology
- One of the most widely adopted cybersecurity frameworks worldwide
- Organizes security into five functions:
- Identify
- Protect
- Detect
- Respond
- Recover
Organizations use it to manage cybersecurity risk and align security strategy with business objectives. (Cloud Security Alliance)
Where it’s used
- U.S. government
- critical infrastructure
- enterprises building secure platforms
2. ISO/IEC 27001
- Global standard for building an Information Security Management System (ISMS)
- Defines organizational security controls across people, process, and technology. (Medium)
Typical domains include:
- risk management
- asset management
- access control
- cryptography
- incident response
Why it’s popular
- internationally recognized certification
- common requirement for SaaS vendors and cloud providers.
3. NIST Risk Management Framework (RMF)
Another framework from National Institute of Standards and Technology.
Purpose:
- integrate security and privacy risk management into system development lifecycle. (Wikipedia)
Core lifecycle:
- Categorize systems
- Select controls
- Implement controls
- Assess controls
- Authorize system
- Monitor continuously
This framework is heavily used in government and defense systems.
4. COBIT
Developed by ISACA.
COBIT focuses on IT governance and management rather than just security.
Major processes include:
- Evaluate, Direct, Monitor
- Align, Plan, Organize
- Build, Acquire, Implement
- Deliver, Service, Support
- Monitor and Assess (Wikipedia)
Often used by CIOs and risk management teams.
Secure Software Development Frameworks (AppSec / DevSecOps)
These frameworks focus on building security into software development.
1. OWASP SAMM
From the Open Web Application Security Project.
SAMM = Software Assurance Maturity Model
It helps organizations evaluate and improve their software security practices across development lifecycle. (OWASP)
Core domains:
- Governance
- Design
- Implementation
- Verification
- Operations
2. NIST Secure Software Development Framework (SSDF)
Published by National Institute of Standards and Technology.
Designed to help organizations integrate security into the SDLC.
Core practices:
- secure design
- code security
- vulnerability management
- supply chain security
This framework gained traction after major supply-chain attacks (e.g., SolarWinds).
3. Microsoft SDL (Security Development Lifecycle)
Developed by Microsoft.
One of the earliest structured secure development lifecycle models.
Key practices:
- threat modeling
- secure coding
- security testing
- incident response
It heavily influenced modern DevSecOps pipelines.
4. BSIMM
BSIMM = Building Security in Maturity Model
Purpose:
- measure and compare software security maturity across organizations
Used by many large technology companies.
AI-Specific Security & Governance Frameworks
AI systems introduce new security risks:
- prompt injection
- model poisoning
- data leakage
- adversarial attacks
- supply-chain attacks
New frameworks are emerging to address these.
1. NIST AI Risk Management Framework (AI RMF)
Created by National Institute of Standards and Technology.
Focus:
- trustworthy AI
- risk governance
- fairness
- robustness
- explainability
Core functions:
- Govern
- Map
- Measure
- Manage
This is currently one of the most influential AI governance frameworks.
2. OWASP Top 10 for LLM Applications
From the Open Web Application Security Project.
Addresses risks unique to generative AI:
Examples:
- prompt injection
- training data poisoning
- model theft
- supply chain vulnerabilities
This has quickly become a de facto security checklist for AI systems.
3. SLSA (Supply-chain Levels for Software Artifacts)
Developed by OpenSSF and Google.
Focus:
- securing software build pipelines
- preventing supply-chain attacks.
Important for AI model pipelines and ML artifacts.
Common Security Design Principles
Many frameworks rely on common design philosophies:
Secure by Design
Security is integrated from the earliest stages of system design, rather than added later. (Wikipedia)
Key principles:
- least privilege
- defense in depth
- minimizing attack surface
This principle is increasingly required by regulators and large organizations.
Quick Comparison
| Category | Framework | Typical Use |
|---|---|---|
| Governance | NIST CSF | enterprise cybersecurity programs |
| Governance | ISO 27001 | compliance / security certification |
| Governance | COBIT | IT governance |
| Risk management | NIST RMF | government / regulated systems |
| Secure development | OWASP SAMM | application security maturity |
| Secure development | NIST SSDF | secure SDLC |
| Secure development | Microsoft SDL | development lifecycle security |
| AI governance | NIST AI RMF | trustworthy AI risk management |
| AI security | OWASP LLM Top 10 | AI application security |
| Supply chain | SLSA | build pipeline security |
✅ In practice, organizations usually combine frameworks.
Example stack:
- ISO 27001 → organizational security program
- NIST CSF → risk management structure
- NIST SSDF / OWASP SAMM → secure software development
- OWASP LLM Top 10 + NIST AI RMF → AI systems security
