Understand code the way an attacker would use it.
AI-powered static application security testing for human-written, AI-generated, and agent-generated software. Aptori SMART understands code in application context, helping teams cut through security noise and focus developers on vulnerabilities that create real risk.
application context.
AI SAST should understand what the code is trying to do.
Traditional static analysis is strong at identifying known code patterns. Modern applications require another layer of reasoning: what does this code mean in the context of the application?
Aptori SMART builds semantic relationships across code, APIs, services, objects, identities, and security decisions. It analyzes how data moves, which paths execute, where trust boundaries exist, and whether business and authorization rules are consistently enforced.
↓ identity context
account_id = params.id
↓ object lookup
account = Account.find(account_id)
↓ missing ownership check
return account.customer_data
semantic result = BROKEN AUTHORIZATION PATH
business context = cross-customer data access
See the security relationships hidden inside the code.
SMART models how code, data, APIs, identities, authorization decisions, and business objects relate to one another—revealing security behavior that isolated rules cannot see.
Find the vulnerabilities that can actually matter.
Traditional SAST is designed to identify suspicious code. Aptori goes further by adding semantic and application context, evaluating the conditions required for exploitation, and using runtime validation where deeper proof is needed. The result is less low-value noise and stronger evidence for what developers should fix first.
Identify suspicious code
Rules, data-flow analysis, and semantic analysis surface code that may represent a security weakness.
Understand the path
Evaluate reachability, call chains, authorization, identity, objects, and application behavior.
Test exploitability
Determine whether the required security conditions can be satisfied and the behavior can be exercised.
Prioritize with evidence
Focus engineering on vulnerabilities supported by application context and exploitability evidence.
AI-generated code changes the volume and velocity of code review.
Coding assistants and development agents can generate implementation logic, APIs, dependencies, and pull requests at machine speed. Security review needs to keep pace without assuming generated code understands your authorization model or business constraints.
Give developers the shortest path from finding to fix.
Once a vulnerability is prioritized, SMART explains the root cause and provides remediation context at the code path where the issue originates. Developers can fix the issue, re-run validation, and close it with evidence.
One platform. Shared application context. Continuous assurance.
SMART is the code layer of Aptori. SGEN adds software supply chain context, SIFT validates APIs and runtime behavior, and DART provides offensive validation—so teams can work from a connected view of application risk rather than separate scanner queues.
AI SAST
Semantic source analysis, data/control flow, authorization, business logic, and remediation.
SMART →SCA
Dependencies, SBOM, CVE/OSV, reachability, EPSS, KEV, containers, and licenses.
SGEN →API Security
Authorization, objects, workflows, business logic, and runtime exploit proof.
SIFT →Pen Testing
Actively exercise attack paths to validate whether weaknesses can become meaningful exploits.
DART →Continue from code analysis into architecture, runtime validation, and secure development.
AI SAST questions.
What is AI SAST?
AI SAST is AI-powered static application security testing. It combines static analysis with semantic code understanding, contextual reasoning, data and control flow analysis, authorization analysis, and AI-assisted remediation.
How is AI SAST different from traditional SAST?
Traditional SAST primarily relies on rules, signatures, and code patterns. AI SAST adds semantic understanding and application context so findings can be interpreted through reachability, authorization, business logic, and remediation context.
Can AI SAST secure AI-generated code?
Yes. The same analysis can be applied to human-written, AI-generated, and agent-generated code before merge or release.
How does Aptori analyze business logic?
Aptori models relationships between application objects, services, APIs, execution paths, authorization controls, workflows, and state so SMART can reason about application-specific security assumptions rather than code syntax alone.
How does runtime validation improve AI SAST?
Runtime validation can confirm whether a static code finding connects to exploitable application or API behavior, giving teams stronger evidence for prioritization and remediation.
.svg){kind=small}
.svg){kind=small}
