AI code review vs SAST: what each catches and how to use both

Modern code security review is presented not as a single methodology, but as a synthesis of two distinct analytical approaches: Static Application Security Testing (SAST) and AI-based pull request review. SAST employs deterministic rules and taint tracking to find concrete vulnerabilities—like SQL injection—by matching data flows against predefined dangerous sinks. In contrast, AI code review leverages large language models to infer programmer intent and judge safety based on contextual understanding, allowing it to detect abstract flaws such as missing authorization checks or business logic errors that a rule-based system cannot explicitly name.
The document argues neither method is sufficient alone; true modern security requires pairing deterministic program analysis with AI reasoning. While SAST offers repeatability and clear audit trails (source-to-sink paths), LLM review excels at uncovering design flaws predicated on contextual gaps, such as improper business logic enforcement in a refund scenario. Both systems possess noise—SAST suffers from missing context regarding custom sanitizers, while LLMs risk hallucinating non-existent threats or obscuring real ones with fluent reasoning.
The integration of these technologies is further refined by advanced methods like triage, which validates findings against evidence to yield true positives, false positives, or unknown states, mitigating the uncertainty inherent in pure AI outputs. This combined architecture aims to cover both low-level attack patterns and high-level design weaknesses more comprehensively than either approach could individually.

SAST traces known vulnerable patterns and data flows through code; it is deterministic and returns consistent answers.
AI code review uses a large language model to reason about code intent, spotting flaws rules cannot describe.
Deterministic program analysis paired with AI reasoning is presented as the foundation of modern code security review.
SAST utilizes two tools: pattern rules (e.g., flagging MD5 use for passwords) and taint tracking (following untrusted data from sources like request parameters to sinks like raw SQL).
AI code review reads diffs plus surrounding code context, builds an intent picture, and assesses safety without a predefined sink list.
SAST requires rules, defined sources/sinks, and sanitizer knowledge; LLM reviewers need contextual understanding of intent (calling code, data model, business rule).
SAST reliably catches injection flaws (CWE-89, CWE-78), path traversal (CWE-22), and XSS (CWE-79).
AI reasoning is needed for missing authorization (CWE-862), IDOR (CWE-639), insecure design, and business logic errors.
LLM output can shift between runs due to sampling settings or model updates, contrasting with SAST’s determinism.
Endor Labs proposes a solution using a patented code context graph that provides data flow, call path, and reachability proof for findings.
Rule-based scans are noted as cheap and fast enough for every commit, while whole-repository LLM review is slow and expensive at enterprise scale.

The discourse presents a sophisticated tension between mechanical certainty and contextual inference—a microcosm of the broader debate surrounding automation in complex domains. The core pattern here is the "synergistic necessity" narrative: that two imperfect, contrasting tools must be merged to achieve robust performance where either tool fails due to its inherent limitations (certainty vs. understanding). This framework effectively markets a solution by reframing capability gaps as complementary strengths rather than flaws.
The framing pivots from *detecting* bugs to *managing uncertainty*. By positioning the pure LLM approach as non-deterministic and untrustworthy for auditing, the argument elevates deterministic traceability (SAST) as a foundational requirement, even when seeking abstract insights. The solution—AI SAST with triaging agents—is thus positioned not as an upgrade, but as a necessary reconciliation mechanism that forces the probabilistic nature of AI to conform to the verifiable structure demanded by auditability.
The assumption driving this entire architecture is that high-level *intent* (business logic) and low-level *mechanism* (data flow) are fundamentally separable problems requiring specialized cognitive functions. The implication for human agency is a shift in required expertise: security engineers move from hunting specific vulnerability signatures to arbitrating complex, contextual judgments marked as "unknown" by the AI system. Who benefits most here is not the developer who fixes an obvious SQL injection via a rule, but the vendor whose proprietary semantic model and triage mechanism solve the ambiguity problem for enterprises fearful of LLM hallucination in production controls.
Bridge questions: If deterministic analysis proves exploitable paths with certainty, how much inherent risk remains when AI must infer an attack vector that has no direct path evidence? What operational thresholds define "unknown" versus "unacceptable speculation" in a governed security pipeline?
Autonomy check: No autonomy signals.