Software analysis used to be relatively straightforward.
Developers wrote code, static analysis scanned it, security teams tested the running application, and code reviewers evaluated important changes.
AI-assisted development has complicated that model.
Modern repositories can contain human-written code, AI-generated functions, AI-assisted refactoring, autonomous coding-agent changes, open-source packages, transitive dependencies, legacy modules, generated configuration, and code whose original provenance is difficult to establish.
That creates a broader question:
How do you actually know what risks exist in a software system?
Static analysis, dynamic analysis, and AI code analysis provide different answers.
Static analysis examines software without executing it. Dynamic analysis examines software while it runs. AI code analysis can add semantic and contextual analysis across code, architecture, dependencies, changes, provenance, and technical debt, depending on how it is implemented.
These approaches should not be viewed as competing replacements.
They are different evidence layers.
The Software Analysis Evidence Stack
A useful way to think about modern software assurance is:
┌──────────────────────────────┐
│ SOFTWARE GOVERNANCE │
│ Enterprise Risk │
└──────────────┬───────────────┘
│
┌──────────────▼───────────────┐
│ AI CODE ANALYSIS │
│ Context • Architecture │
│ Intent • Provenance • Debt │
└──────────────┬───────────────┘
│
┌──────────────▼───────────────┐
│ DYNAMIC ANALYSIS │
│ Runtime • APIs • Config │
│ Behavior • Exploitability │
└──────────────┬───────────────┘
│
┌──────────────▼───────────────┐
│ STATIC ANALYSIS │
│ Code • Patterns • Data Flow │
│ Vulnerabilities • Quality │
└──────────────────────────────┘
The important point is that each layer produces different evidence.
A clean SAST scan does not prove that an application is secure.
A clean DAST scan does not prove that the architecture is maintainable.
An AI code review does not prove that runtime behavior is safe.
The goal is to combine evidence.
What Is Static Analysis?
Quick answer
Static analysis examines source code, binaries, configuration, or other software artifacts without executing the application. SAST, or Static Application Security Testing, is the security-focused form of static analysis. It can identify potentially vulnerable code patterns, insecure APIs, dangerous data flows, hard-coded secrets, and coding-quality problems before software reaches production.
Static analysis is one of the oldest and most established forms of automated software analysis.
Tools can inspect code using techniques such as:
- Abstract syntax trees
- Control-flow analysis
- Data-flow analysis
- Taint analysis
- Pattern matching
- Security rules
- Quality rules
- Dependency analysis
- Configuration analysis
For example, consider:
query = “SELECT * FROM users WHERE id=” + user_id
database.execute(query)
A static analyzer may identify a potentially unsafe data flow from an external input into a database query.
That is useful because the application does not have to be running.
What static analysis does well
Static analysis is particularly useful for identifying:
- Injection patterns
- Hard-coded credentials
- Dangerous functions
- Insecure API usage
- Weak cryptographic implementations
- Certain data-flow vulnerabilities
- Security-policy violations
- Coding-standard violations
- Dependency relationships
- Some configuration problems
It can also provide code-quality signals such as complexity, duplication, unreachable code, or problematic patterns.
OWASP describes static analysis as an important mechanism for identifying potential security weaknesses in source code, while also recognizing that automated analysis has limitations and requires analyst interpretation. (OWASP)
What static analysis misses
Static analysis cannot inherently observe everything that happens after deployment.
It can struggle with:
- Runtime-only behavior
- Environment-specific configuration
- External service interactions
- Undiscovered execution paths
- Complex business logic
- Deployment-specific vulnerabilities
- Operational conditions
- Human processes
It can also produce false positives and false negatives.
That distinction is critical.
Static finding
≠
Confirmed vulnerability
≠
Enterprise risk
A scanner identifies a potential condition.
Someone still needs to determine whether it is exploitable, relevant, reachable, material, and worth prioritizing.
Executive Insight: Static analysis gives you evidence about the software artifact. It does not give you complete evidence about the deployed system.
Executive Takeaway: Use SAST early and continuously, but do not interpret a clean scan as proof of application security.
What Is Dynamic Analysis?
Quick answer
Dynamic analysis evaluates software while it is executing. DAST, or Dynamic Application Security Testing, commonly interacts with running web applications and APIs to observe responses, authentication behavior, sessions, exposed functionality, configuration, and potential runtime vulnerabilities. Unlike SAST, DAST can provide evidence about how a deployed application actually behaves.
Imagine an application exposes:
POST /api/login
GET /api/users
GET /api/admin
POST /api/payment
Dynamic analysis can interact with these endpoints and observe how the application responds.
This allows testing of conditions such as:
- Authentication
- Authorization
- Session handling
- Input validation
- API behavior
- HTTP responses
- Exposed services
- Runtime configuration
- Certain injection vulnerabilities
- Runtime security controls OWASP’s DevSecOps guidance treats DAST as a distinct security-testing activity because runtime testing provides evidence that source analysis cannot establish on its own. (OWASP)
Where DAST is stronger
Suppose source code contains:
if user.is_admin:
return admin_data()
Static analysis may not know whether the authentication and authorization system correctly establishes user.is_admin.
A runtime test can attempt to access the endpoint using different identities and observe the application’s actual behavior.
That is a fundamentally different type of evidence.
What dynamic analysis misses
DAST also has limitations.
The application must be running.
Testing depends on:
- Reachable endpoints
- Test credentials
- Application state
- Test coverage
- Authentication setup
- Discovered workflows
- Environment configuration A DAST tool may never reach a vulnerable function hidden behind a complex business workflow.
And DAST generally cannot tell you that a codebase has excessive coupling, poor maintainability, or decades of accumulated technical debt.
Executive Insight: DAST validates runtime exposure and behavior, not the overall quality or architecture of the source code.
Executive Takeaway: Use dynamic analysis to validate what static analysis cannot establish: how the running system behaves.
What Is AI Code Analysis?
Quick answer
AI code analysis uses machine-learning and large-language-model techniques to analyze source code and related software artifacts for semantic, contextual, architectural, security, quality, and governance insights. Its capabilities depend heavily on implementation. Enterprise AI code analysis can potentially connect code, dependencies, repository history, architecture, ownership, provenance, and technical debt rather than examining isolated code patterns alone.
This distinction matters.
There is a big difference between:
“Review this 50-line function.”
and:
“Analyze this repository and identify:
architectural inconsistencies
risky dependencies
duplicated business logic
technical debt
suspicious changes
ownership gaps
provenance concerns
security issues
AI-generated code patterns”
The second task requires substantially more context.
AI code analysis can potentially help reason about:
- Code intent
- Architectural relationships
- Business logic
- Dependency usage
- Technical debt
- Duplication
- Maintainability
- Documentation gaps
- Ownership
- Provenance
- Security patterns
- AI-generated code
- Architectural consistency But “AI” does not automatically mean “better.”
An AI system can misunderstand code.
It can lack critical context.
It can generate incorrect conclusions.
It can also be overly confident.
Therefore:
AI analysis
↓
Potential insight
↓
Human validation
↓
Risk decision
GitHub’s documentation similarly emphasizes understanding the limitations and responsible use of AI-powered development and security capabilities. (GitHub)
Executive Insight: AI code analysis is best viewed as contextual intelligence layered on top of software evidence.
Executive Takeaway: AI can expand the scope and context of analysis, but deterministic testing and human judgment remain necessary.
Static Analysis vs Dynamic Analysis vs AI Code Analysis
Quick answer
Static analysis examines code without executing it. Dynamic analysis examines behavior while software runs. AI code analysis can provide semantic and contextual interpretation across a broader set of software artifacts. The three methods overlap, but they answer different questions and therefore should be combined rather than treated as interchangeable.
The distinction can be simplified:
Static analysis asks:
What patterns and structures exist in the software?
Dynamic analysis asks:
What happens when the software runs?
AI code analysis asks:
What does the software appear to do, how do its components relate, and what risks become visible when the code is interpreted in context?
None of these questions replaces the others.
Executive Insight: The value comes from combining evidence, not choosing one “best” analyzer.
Executive Takeaway: Select analysis methods based on the uncertainty you need to reduce.
What Can Each Analysis Method Detect?
Quick answer
Every analysis method has blind spots. SAST is particularly effective for source-level patterns and data flows. DAST is particularly effective for runtime behavior and exposed application functionality. AI analysis can potentially connect technical findings with architecture, dependencies, provenance, and maintainability. These are capabilities, not guarantees.
The important word is potential.
A capability matrix should never be interpreted as:
“AI detects this.”
It should be interpreted as:
“This analytical approach can contribute evidence relevant to this risk.”
That distinction prevents overclaiming.
Executive Insight: Detection capability is not the same as guaranteed detection.
Executive Takeaway: Evaluate the completeness and reliability of the evidence, not simply the presence of an AI or security scanner.
Why AI-Generated Code Changes Software Assurance
Quick answer
AI-generated code introduces a new assurance dimension: provenance. Enterprises need to understand which code was generated or modified by AI, how it was validated, who approved it, what tests were performed, and whether the resulting software remains consistent with security and architectural requirements. AI-generated code is not inherently insecure, but it requires disciplined validation.
AI-assisted development can involve:
- GitHub Copilot
- AI pair-programming
- LLM-generated functions
- AI-generated tests
- AI-assisted refactoring
- Autonomous coding agents
- Agentic software-development workflows The risks are not fundamentally different from existing software risks.
The challenge is that AI can produce software faster and at a scale that makes traditional assumptions about authorship and review less reliable.
Potential problems include:
Hallucinated APIs
An AI model may generate code against an API that does not behave as assumed.
Insecure patterns
Generated code can contain insecure authentication, authorization, input validation, cryptography, or error-handling patterns.
Outdated APIs
Generated code can rely on obsolete approaches.
Weak tests
AI-generated tests can create the appearance of coverage without adequately exercising important failure conditions.
Unnecessary dependencies
A generated implementation may introduce a package where a simpler implementation would have been preferable.
Architecture inconsistency
Generated code may solve the local problem while violating system-level architecture.
Provenance uncertainty
The organization may not have clear evidence of which code was generated, reviewed, tested, or approved.
NIST’s secure-development guidance for generative AI specifically extends SSDF practices to AI-related development. (NIST)
Microsoft similarly recommends reviewing and testing AI-assisted code rather than assuming generated output is safe. (Microsoft)
A useful model is:
AI / Human
↓
Generated or Modified Code
↓
Source Control
↓
SAST + Dependency Analysis
↓
AI Code Analysis
↓
Human Review
↓
Automated Tests
↓
DAST / Runtime Validation
↓
Approval
↓
Production
The important question becomes:
Which code was generated, modified, validated, reviewed, tested, and approved?
Executive Insight: AI-assisted development adds an authorship and provenance problem to an already complex software supply chain.
Executive Takeaway: Treat AI-generated code as normal production code with additional provenance and validation requirements.
How to Build an Enterprise Software Analysis Framework
Quick answer
A practical enterprise framework should combine multiple analysis techniques and then convert their findings into validated governance decisions. A useful model is Analyze → Correlate → Validate → Govern → Monitor. The framework prevents organizations from treating individual scanner findings as complete representations of software risk.
1. Analyze
Collect evidence from:
- Static analysis
- Dynamic analysis
- AI code analysis
- Dependency analysis
- Secrets detection
- SBOMs
- Repository history
- Architecture analysis
- Testing
2. Correlate
Connect findings across:
Code
↓
Dependencies
↓
Architecture
↓
Runtime
↓
Provenance
↓
Ownership
↓
Technical Debt
3. Validate
Ask:
- Is the finding exploitable?
- Is the affected component deployed?
- Is it reachable?
- Is it business-critical?
- Does architecture mitigate or amplify the issue?
- Is there an accountable owner?
- Does the issue violate policy?
- Does it affect compliance or licensing?
4. Govern
Establish:
- Risk thresholds
- Branch policies
- Code ownership
- Approval requirements
- AI coding policies
- Dependency rules
- Release gates
- Exception processes
5. Monitor
Continuously reassess:
- Code changes
- Vulnerabilities
- Dependencies
- Runtime exposure
- Provenance
- Architecture drift
- Technical debt
- AI-generated changes
ANALYZE
↓
Static + Dynamic + AI Analysis
↓
CORRELATE
↓
Code + Dependencies + Runtime + Architecture
↓
VALIDATE
↓
Security + Business + Compliance + Operations
↓
GOVERN
↓
Policies + Gates + Ownership + Approval
↓
MONITOR
↓
Continuous Software Assurance
flowchart TD
A[Analyze] → B[Correlate]
B → C[Validate]
C → D[Govern]
D → E[Monitor]
E → A
A → A1[Static Analysis]
A → A2[Dynamic Analysis]
A → A3[AI Code Analysis]
A → A4[Dependency Analysis]
A → A5[SBOM and Secrets]
B → B1[Code]
B → B2[Dependencies]
B → B3[Runtime]
B → B4[Architecture]
B → B5[Provenance]
B → B6[Ownership]
C → C1[Security]
C → C2[Business Requirements]
C → C3[Compliance]
C → C4[Operations]
D → D1[Policies]
D → D2[Approval Gates]
D → D3[Code Ownership]
D → D4[Release Controls]
E → E1[Code Changes]
E → E2[Runtime Behavior]
E → E3[Dependency Changes]
E → E4[Architecture Drift]
E → E5[Technical Debt]
Executive Insight: The analysis layer produces evidence; the governance layer determines what that evidence means for the organization.
Executive Takeaway: Build an evidence pipeline instead of deploying disconnected scanners.
AI Code Audit vs Traditional Code Review
Quick answer
Traditional code review usually focuses on a change set and relies primarily on human engineering judgment. An AI code audit can extend analysis across an entire repository, dependency graph, architecture, history, and technical-debt profile. It should complement human review rather than replace it.
A traditional reviewer might ask:
“Does this change work?”
An AI-assisted audit can additionally ask:
“Does this change duplicate existing functionality?”
“Does it introduce a new dependency?”
“Does it increase coupling?”
“Does it violate existing architecture?”
“Does it introduce a new security-sensitive flow?”
“Does the repository contain evidence that this code was AI-generated or AI-assisted?”
The answer to those questions still requires validation.
Executive Insight: AI expands the review surface, but human expertise remains responsible for final interpretation.
Executive Takeaway: Use AI audits to increase context and coverage, not to remove engineering accountability.
The Software Analysis Evidence Chain
Quick answer
Individual findings become much more useful when connected to dependencies, provenance, architecture, ownership, and business impact. A vulnerability identifier alone tells an executive relatively little. A connected evidence chain can explain why a technical finding matters and what should happen next.
Consider:
Code
↓
Dependencies
↓
Provenance
↓
Architecture
↓
Security
↓
Quality
↓
Ownership
↓
Governance
↓
Business Risk
For example:
Unknown Dependency
↓
No Clear Maintainer
↓
Delayed Security Patch
↓
Production Exposure
↓
Operational Risk
↓
Business Continuity Risk
Or:
High Coupling
↓
Difficult Testing
↓
Slow Change Validation
↓
Higher Change Failure Risk
↓
Operational Exposure
This is where code intelligence becomes useful.
The Code Registry’s broader software-governance perspective is based on the idea that code should be understood as an enterprise asset rather than only as a collection of files.
Executive Insight: The business meaning of a finding often emerges from its relationships with other software entities.
Executive Takeaway: Connect technical findings to ownership, exposure, architecture, dependencies, and business consequences.
How SAST, DAST, and AI Analysis Fit Into DevSecOps
Quick answer
DevSecOps is the operating model that integrates security into software development and delivery. SAST, DAST, dependency analysis, secrets scanning, AI code analysis, testing, and provenance controls are evidence-producing capabilities within that model. None of these individual technologies constitutes DevSecOps by itself.
A simplified lifecycle looks like:
Plan
↓
Develop
↓
AI-Assisted Development
↓
SAST + Secrets + Dependencies
↓
Code Review + AI Analysis
↓
Build + Provenance
↓
DAST + Runtime Testing
↓
Release Governance
↓
Production Monitoring
OWASP’s DevSecOps guidance treats SAST and DAST as distinct activities within the broader software-development lifecycle. (OWASP)
NIST’s SSDF similarly provides practices that can be integrated into existing development processes rather than functioning as a single standalone security product. (NIST)
Executive Insight: DevSecOps is the system of controls; scanners and analysis engines are components within that system.
Executive Takeaway: Integrate analysis into development, CI/CD, deployment, and governance rather than treating security as a final-stage scan.
Software Supply Chain, Dependencies, and Provenance
Quick answer
Modern applications depend on far more than their own source code. Direct packages, transitive dependencies, build tools, registries, commercial libraries, containers, and third-party services all contribute to software risk. SBOMs improve visibility into components, while provenance technologies such as SLSA provide evidence about software production. Neither is a complete risk assessment.
An enterprise dependency inventory should account for:
Application
├── First-party code
├── Direct dependencies
│ └── Transitive dependencies
├── Build dependencies
├── Runtime dependencies
├── Commercial components
└── External services
An SBOM can help answer:
What components are present?
It does not automatically answer:
Are they exploitable?
Are they reachable?
Are they maintained?
Who owns them?
Are they correctly configured?
Are they appropriately licensed?
Are they actually deployed?
CISA describes SBOMs as an important software-supply-chain transparency mechanism. (CISA)
SLSA focuses on software supply-chain integrity and provenance, including verifiable information concerning how software artifacts are produced. (SLSA)
This produces an important distinction:
SBOM
↓
Inventory
SLSA / Provenance
↓
Production Evidence
Static Analysis
↓
Code Evidence
Dynamic Analysis
↓
Runtime Evidence
AI Analysis
↓
Contextual Evidence
Executive Insight: An inventory tells you what exists. Assurance requires understanding how those components are used, produced, exposed, maintained, and governed.
Executive Takeaway: Combine SBOM, provenance, dependency analysis, static analysis, runtime evidence, and ownership information.
Technical Debt and Architecture Risk
Quick answer
A software system can have no critical vulnerability findings and still represent significant enterprise risk. Excessive coupling, duplicated logic, obsolete abstractions, fragile interfaces, dead code, poor testability, undocumented integrations, and architecture drift can make software difficult and expensive to change. Technical debt should therefore be evaluated alongside security findings.
Consider:
No Critical Vulnerabilities
+
High Architectural Coupling
+
Poor Testability
+
Unclear Ownership
↓
High Change Risk
Conventional security scanners may not recognize this as a critical security issue.
But from an engineering or acquisition perspective, it can be material.
AI code analysis can potentially help identify semantic relationships across large repositories, including:
- Duplicate business logic
- Similar implementations
- Architectural inconsistencies
- Highly coupled modules
- Unused or obsolete code
- Documentation gaps
- Fragile interfaces
- Ownership gaps These findings still require engineering validation.
The question is not:
“Does AI understand the architecture perfectly?”
It is:
“Can AI help engineers discover relationships and patterns that deserve investigation?”
That is a much more defensible use of AI.
Executive Insight: Technical debt is not merely a developer inconvenience; it can affect operational resilience, change risk, maintenance cost, and software value.
Executive Takeaway: Assess architecture and technical debt even when conventional vulnerability scans are clean.
Common Mistakes
Quick answer
Software assurance frequently fails because organizations confuse a particular security control with complete assurance. SAST, DAST, AI analysis, code review, penetration testing, SBOMs, and dependency scanning each provide different evidence. The correct response to a clean result is not “the software is safe,” but “this particular analysis did not identify the conditions it was designed to find.”
Building an AI-Assisted Software Assurance Program
Quick answer
Organizations adopting AI-assisted development should extend existing secure-SDLC controls rather than creating an isolated “AI security” process. AI-generated code should pass through repository governance, source analysis, dependency controls, testing, human review, provenance controls, runtime validation, and release gates.
A practical control model is:
AI Coding Policy
↓
Repository Governance
↓
AI-Generated Change
↓
SAST + Secrets + Dependencies
↓
AI Code Analysis
↓
Human Review
↓
Automated Tests
↓
Build Provenance
↓
DAST / Runtime Testing
↓
Release Gate
↓
Continuous Monitoring
Relevant governance areas include:
- NIST SSDF
- OWASP guidance
- CISA software-supply-chain guidance
- SLSA
- SBOM
- Secrets management
- Branch protection
- Code ownership
- Dependency governance
- Policy as code
- Human approval
- Automated testing
- Provenance
- Continuous monitoring The objective is not to stop developers from using AI.
It is to ensure that AI-generated output enters an accountable engineering process.
Executive Insight: AI development governance should focus on evidence and controls rather than banning a development technique simply because it involves AI.
Executive Takeaway: Apply the same production-quality bar to AI-generated code, while adding provenance and review controls.
From Periodic Code Review to Continuous Software Intelligence
Quick answer
AI-assisted development increases the speed and volume of software changes. This makes periodic review less sufficient as a standalone assurance model. The likely direction is toward continuous code intelligence: continuously analyzing changes, dependencies, architecture, provenance, technical debt, and runtime evidence and using that information to support automated governance.
The conceptual progression is:
Periodic Code Review
↓
Automated Code Scanning
↓
Continuous Code Intelligence
↓
Continuous Software Assurance
Several trends are particularly relevant:
- Autonomous coding agents
- AI-assisted software development
- AI software assurance
- Continuous provenance
- Policy as code
- Internal developer platforms
- Platform engineering
- Automated dependency governance
- Continuous software inventories
- Automated technical-debt analysis
- AI-assisted DevSecOps The important shift is not simply from human analysis to AI analysis.
It is from:
Periodic inspection
to:
Continuous evidence
As software changes continuously, assurance must also become continuous.
Executive Insight: Faster software production increases the need for continuous evidence rather than eliminating traditional controls.
Executive Takeaway: The long-term objective should be continuous software assurance, not simply more frequent scanning.
A Practical Enterprise Model
For organizations trying to operationalize all three analysis methods, the following model provides a useful starting point:
SOFTWARE
│
┌────────────┼────────────┐
│ │ │
STATIC DYNAMIC AI
ANALYSIS ANALYSIS ANALYSIS
│ │ │
▼ ▼ ▼
CODE RUNTIME CONTEXT
EVIDENCE EVIDENCE EVIDENCE
│ │ │
└────────────┼────────────┘
▼
CORRELATION
│
▼
HUMAN VALIDATION
│
▼
SOFTWARE GOVERNANCE
│
▼
BUSINESS RISK
This model works for more than application security.
It can support:
- Software due diligence
- Acquisition assessments
- Technical-debt analysis
- Architecture reviews
- AI code audits
- Software supply-chain assessment
- Software asset valuation
- Executive software risk reporting The Code Registry can be positioned naturally within this broader discipline as an example of how code intelligence can help organizations connect technical software evidence with governance and business decisions.
This model works for more than application security.
It can support:
- Software due diligence
- Acquisition assessments
- Technical-debt analysis
- Architecture reviews
- AI code audits
- Software supply-chain assessment
- Software asset valuation
- Executive software risk reporting The Code Registry can be positioned naturally within this broader discipline as an example of how code intelligence can help organizations connect technical software evidence with governance and business decisions.
Key Takeaways
Quick answer
Static analysis, dynamic analysis, and AI code analysis should not be treated as competing technologies. They provide different evidence about the same software system. Strong software assurance combines source-level analysis, runtime validation, contextual intelligence, dependency intelligence, provenance, architecture analysis, human judgment, and governance.
- Static analysis identifies what patterns and structures exist in software.
- Dynamic analysis identifies how software behaves under runtime conditions.
- AI code analysis can add semantic and contextual intelligence.
- AI code analysis should not replace SAST.
- AI code analysis should not replace DAST.
- AI-generated code requires the same security and quality controls as other production code.
- AI-assisted development creates additional provenance and approval requirements.
- SBOMs improve transparency but do not constitute complete software risk assessments.
- Technical debt and architecture risk can be material even without vulnerabilities.
- Software analysis becomes more valuable when findings are connected to ownership, dependencies, architecture, provenance, and business impact. The core model is:
Static Analysis
+
Dynamic Analysis
+
AI Code Analysis
+
Dependencies
+
Provenance
+
Architecture
+
Human Validation
↓
Software Assurance
↓
Software Governance
Conclusion
Quick answer
Static analysis, dynamic analysis, and AI code analysis answer different questions. Static analysis tells you what patterns exist in the code. Dynamic analysis tells you how the application behaves when it runs. AI code analysis can help explain the broader context connecting code, architecture, dependencies, provenance, quality, and governance. The strongest enterprise model combines all three.
The wrong question is:
Which analysis method is better?
The better question is:
What evidence do we have, what evidence is missing, and what does that mean for the software risk decision?
For a modern application, assurance may require evidence from:
Source Code
↓
Dependencies
↓
Architecture
↓
Provenance
↓
Static Analysis
↓
Dynamic Analysis
↓
AI Code Analysis
↓
Human Review
↓
Governance
↓
Business Risk
This becomes especially important when software includes AI-generated code, autonomous coding-agent changes, open-source dependencies, legacy systems, third-party components, and undocumented architecture.
The Code Registry’s role in this broader landscape is best understood through the concept of code intelligence: helping organizations move from isolated code findings toward a more complete understanding of software assets, risk, maintainability, provenance, and governance.
For teams evaluating AI-assisted development, an AI Code Risk Assessment or enterprise software/code review can provide a structured starting point. Organizations can also use the framework in this article to evaluate their software governance maturity before adopting additional analysis capabilities.
Ultimately, software assurance is not about proving that software contains zero risk.
It is about building enough trustworthy evidence to understand the risk that exists, the risk that remains uncertain, and the controls available to manage it.
Frequently Asked Questions
What is the difference between static analysis and dynamic analysis?
Static analysis examines source code or software artifacts without execution. Dynamic analysis examines an application while it runs. Static analysis provides evidence about code patterns and data flows, while dynamic analysis provides evidence about runtime behavior, exposed functionality, configuration, and certain exploitable conditions.
They are complementary rather than interchangeable.
What is static code analysis?
Static code analysis examines software without executing it. It can identify potentially insecure code patterns, coding defects, dependency relationships, data-flow problems, and quality issues. SAST is the security-focused implementation of static analysis and is commonly integrated into IDEs, pull requests, and CI/CD pipelines.
What is dynamic code analysis?
Dynamic code analysis evaluates software while it is executing. DAST is a common application-security implementation that interacts with running applications and APIs to identify potential runtime vulnerabilities, configuration weaknesses, authentication problems, and exposed functionality.
What is the difference between SAST and DAST?
SAST analyzes code without running the application, while DAST tests the running application. SAST can identify source-level vulnerabilities and insecure patterns; DAST can identify runtime behavior and exposure that source analysis cannot establish. Enterprise application-security programs commonly use both because they provide different evidence.
What is AI code analysis?
AI code analysis applies AI and machine-learning techniques to source code and related artifacts. Depending on implementation, it can analyze semantics, architecture, dependencies, technical debt, business logic, provenance, and security patterns. Its conclusions should be validated because AI systems can misunderstand code or lack important context.
Can AI code analysis replace SAST?
AI code analysis should not replace SAST. SAST provides repeatable source-level analysis for known security and quality patterns. AI can provide additional semantic interpretation and contextual correlation. Using both can provide broader evidence than either approach alone.
Can AI code analysis replace DAST?
AI code analysis cannot inherently replace DAST because it does not automatically establish runtime behavior or exploitability. DAST interacts with a running application and provides evidence about actual runtime responses, exposed interfaces, configuration, and authentication behavior.
How should enterprises analyze AI-generated code?
AI-generated code should pass through the same production controls applied to human-written code: source analysis, dependency analysis, secrets detection, automated testing, human review, runtime testing, and release governance. Enterprises should additionally consider provenance, authorship, AI-assisted changes, validation evidence, and approval history.
What can static analysis miss?
Static analysis can miss runtime-only vulnerabilities, deployment-specific configuration problems, inaccessible execution paths, complex business-logic flaws, external-service interactions, and conditions that depend on runtime state. It can also generate false positives and false negatives, so findings require validation.
How can enterprises combine static, dynamic, and AI code analysis?
Use static analysis for source-level evidence, dynamic analysis for runtime evidence, and AI code analysis for semantic and contextual analysis. Correlate those findings with dependencies, architecture, provenance, ownership, technical debt, business requirements, and risk tolerance before making governance decisions.
