AI-generated code has quietly moved from novelty to default. What started as autocomplete and helper snippets is now full features, workflows, and entire services written by models. For many teams, AI is no longer “assisting” development – it is actively shaping application behavior.
That shift changes the risk profile of software in subtle but important ways.
Most AI-generated code looks fine at first glance. It compiles. It passes basic tests. It often reads cleanly and confidently. But that surface quality can be misleading. The real problems tend to show up in how the code behaves under stress, misuse, or unexpected input – the exact conditions attackers rely on.
Traditional review practices were built for human-written code. They assume intent, familiarity with the domain, and an understanding of the trade-offs behind a design decision. AI-generated code breaks those assumptions. Reviewing it effectively requires a slightly different mindset.
The goal is not to distrust AI blindly. The goal is to recognize that AI changes where risk hides – and to adapt review practices accordingly.
Start With the Right Mental Model
The most common mistake teams make is treating AI-generated code like code written by a junior developer who “just needs guidance.” That framing is inaccurate and dangerous.
AI does not reason about threat models. It does not understand your organization’s security posture. It does not know which workflows are sensitive or which shortcuts are unacceptable. It predicts plausible code, not safe behavior.
That means reviewers need to adjust their expectations. When reviewing AI-generated code, the question should not be “Does this look reasonable?” The question should be “What assumptions is this code making, and are those assumptions safe?”
AI often fills in gaps by guessing. If a requirement is ambiguous, the model will still produce something. That “something” may work functionally while violating security boundaries in ways that are hard to spot during a normal review.
The first best practice, then, is mindset: assume the code is confidently incomplete. It may be correct in the happy path and dangerously vague everywhere else.
Treat AI-Generated Code as Untrusted by Default
AI-generated code should be reviewed the same way you would review code copied from an external repository or pasted from an online forum.
That does not mean it is bad code. It means it did not come with intent, accountability, or context.
Many security incidents begin with “we assumed this was fine.” AI output invites that assumption because it often looks polished. Reviewers skim instead of interrogate. That is exactly where risk slips through.
Untrusted does not mean adversarial. It means the burden of proof shifts. The reviewer is not validating the author’s judgment – they are validating the behavior of the system.
In practice, this means:
- Slowing down on AI-written sections, even when they look clean
- Asking why a particular approach was chosen
- Questioning defaults, fallbacks, and error handling
- Treating convenience patterns as suspicious until proven safe
This is especially important for glue code – the parts that connect APIs, auth systems, databases, and external services. AI is very good at stitching things together. It is much worse at understanding the security implications of those stitches.
Review Behavior, Not Just Syntax
Traditional code review focuses heavily on structure: function boundaries, variable naming, error handling, and style. Those things still matter, but they are not where AI-related risk usually lives.
AI-generated vulnerabilities tend to be behavioral. They emerge from how components interact over time, not from a single obviously dangerous line.
For example:
- A permission check exists, but it only runs on one code path
- A workflow assumes that a previous step always happened
- An API trusts the client-provided state that should be server-derived
- A retry mechanism replays sensitive actions without revalidation
None of these stand out syntactically. They look reasonable. They even look intentional. But they fail when someone uses the system in a way the original prompt did not anticipate.
An effective review means mentally executing the code as an attacker would. What happens if steps are skipped? What happens if requests are replayed? What happens if inputs arrive out of order?
AI often optimizes for linear flows. Attackers exploit non-linear ones.
Be Extra Strict Around Auth, Authorization, and State
If there is one area where AI consistently struggles, it is security boundaries.
Authentication, authorization, session handling, and state transitions require an understanding of who is allowed to do what and when. AI models tend to flatten these distinctions.
Common issues reviewers should actively look for include:
- Authorization checks tied to UI logic instead of server logic
- Role checks that assume a fixed set of roles
- Trust in client-supplied identifiers or flags
- Session state reused across unrelated actions
- “Temporary” bypasses are left in place
These problems are rarely malicious. They are the result of AI filling in gaps with patterns that work functionally but fail defensively.
Reviewers should treat any AI-generated code that touches identity, access, or state as high-risk by default. That does not mean rejecting it – it means reviewing it with far more scrutiny than usual.
Ask simple but uncomfortable questions:
- What prevents a user from calling this directly?
- What enforces this rule if the UI is bypassed?
- What happens if the state is manipulated?
If the answers are vague, the code is not ready.
Demand Evidence, Not Explanations
One subtle shift AI introduces is confidence without proof. AI-generated code often explains itself well. Comments are clear. Logic is neatly structured. Everything looks intentional.
That is not evidence.
A reviewer should not accept “this should be safe” as a valid conclusion. Especially not when the code was generated by a system that cannot test or observe runtime behavior.
For high-risk areas, evidence matters more than explanation. Evidence can include:
- Tests that demonstrate the enforcement of boundaries
- Reproduction steps for edge cases
- Dynamic validation that confirms behavior under misuse
- Logs or metrics that show how the code behaves in practice
This is where many teams struggle. They approve AI-generated changes based on readability and perceived correctness, not on demonstrated behavior.
That gap becomes expensive later.
Keep Human Ownership Explicit
One of the most dangerous patterns emerging with AI-generated code is unclear ownership. Code appears in a repository, works well enough, and no one feels responsible for it.
When something breaks – or worse, when a vulnerability is discovered – the response is often confusion. Who understands this logic? Who can safely modify it? Who is accountable?
Every piece of AI-generated code should have a clear human owner. Someone who can explain what it does, why it exists, and how to fix it if needed.
This is not a bureaucratic requirement. It is a survivability one. Code without ownership becomes technical debt instantly. AI accelerates that problem because it lowers the friction to creating complexity.
Good review culture makes AI assistance visible, not invisible. Reviewers should ask who owns the logic, not just whether it passes tests.
Integrate Security Review Earlier, Not Later
Many teams try to “add security review” after AI-generated code is written. That approach rarely works.
AI changes code faster than traditional review cycles can keep up. By the time security detects the change, it is often already merged, deployed, or relied upon elsewhere.
The teams that handle this well integrate security signals earlier:
- Security checks run automatically on AI-generated changes
- High-risk patterns trigger additional review
- Runtime testing validates behavior before release
- Feedback loops are short and actionable
This is not about slowing development. It is about keeping pace with it. AI speeds up writing code. Security has to move at the same speed or become irrelevant.
Final Thoughts: Speed Changes Responsibility, Not Risk
AI-generated code is not inherently unsafe. However, it shifts where risk appears and how easily it can be hidden.
Teams that review AI-generated code the same way they review human-written code will miss things. Not because they are careless, but because the assumptions no longer hold.
Effective review requires skepticism, curiosity, and a focus on behavior over appearance. It requires treating AI output as powerful but incomplete – something to be validated, not trusted by default.
The teams that get this right will move faster and safer. The ones that do not will discover the cost later, usually in production.
AI can help write code quickly. It does not reduce the responsibility to understand, defend, and own it.
