Synthetic Intelligence in Software Lifecycle: Automating the Detection of Logical Vulnerabilities

In recent years, the fintech development industry has faced a unique phenomenon: while the quality of testing tools is rapidly improving, the number of critical incidents related to logical errors remains unchanged.

Analytical summaries and reports on incidents in fintech, Web3 projects, and commercial systems reveal significant figures. For example, over 60% of serious corruptions are caused not by syntax bugs, but by business logic errors. In other words, everything may function correctly, but the expected system behavior is disrupted.

This is where synthetic intelligence (SSI) comes in. It's a type of artificial intelligence system that has the ability not only to analyze code but also to generate its meaning, context, and the intentions embedded in the program by developers.

The Limits of Classical Testing: Why Logical Errors Remain Invisible

Traditional tools such as SAST (static application security testing), linters, fuzzers, and unit tests are excellent at their job descriptions. However, this is implemented solely within the scope of:

SAST looks for known vulnerability patterns (buffer overflows, SQL injections, insecure calls);
Unit tests verify expected behavior in predefined scenarios;
Fuzzing generates random or semi-random input data.

The paradox is that business logic errors are not errors from the compiler's point of view. While standard static analysis tools excel at pattern matching, they fail to grasp the developer's intent. This is where architecting custom AI solutions becomes an infrastructural necessity. By building cognitive layers that can simulate execution flows and reason about business constraints, engineering teams can identify hidden abuse scenarios even before the code enters production. For example:

the transaction is allowed to be carried out in the wrong sequence;
the user role is only partially checked;
the status check occurs before a critical situation occurs, not after it is recorded;
two correct calls in the wrong order create a vulnerability.

For classical tools, such logic is considered normal code elements. They don't understand why this code exists or what process it describes.

AI as a Cognitive Auditor: Analyzing Logic, Not Lines of Code

Synthetic intelligence analyzes all situations using a different approach. It doesn't identify bad lines of code, but rather builds a model of system behavior.

Modern LLM-oriented security systems include several analytical levels, namely:

Data Flow Graph (DFG) – how data changes between functions, services, and states;
Control flow graph (CFG) – how certain industry logics are executed under certain conditions;
State Machine Reconstruction – restoration of hidden business logic (statuses, roles, process phases);
Semantic analysis – interpretation of link names, comments, API contracts.

Thus, synthetic intelligence answers the question of whether the actual behavior of the code is consistent with the rules and laws of operation of the entire system.

In 2025, numerous studies showed that AI logic analysis identifies 35-50% more critical scenarios than using SAST and other tools in combination with manual auditing in complex systems.

Integration into the project life cycle (SDLC): preventive analysis before deployment

The key value of artificial intelligence is not that it checks the system after an error has occurred, but that it allows it to be prevented at the early stages of an audit.

In full-fledged teams, AI agents are integrated directly into the SDLC (software development life cycle). This happens as follows:

At the query stage, AI analyzes how the new code changes existing logic circuits, not just the differential.
In CI/CD sequences, the system runs thousands of simulated execution scenarios before deployment.
In the security system, a release is blocked not because of an error, but because of a violation of logic options (for example, when a user cannot receive an asset until payment is confirmed, and other situations).

According to experts researching application security (AppSec), such integration reduces the cost of fixing logical bugs by 4-6 times compared to their detection during the production cycle.

A practical case: security logic and “race conditions” in the escrow mechanism

As an example, let's consider a situation with an escrow service. The logic involves the following steps:

the user deposits funds;
the system is waiting for confirmation of the incident;
funds will be unblocked.

Formally, such code may be correct. However, synthetic intelligence may detect the following risks:

the ability to call in parallel «releaseFunds()» to update the status;
lack of atomicity between state checking and balance changes;
a scenario where the timeout and manual proof are triggered immediately.

The artificial intelligence model runs tens of thousands of event order variations that humans or unit tests would be physically unable to handle. In real-world cases, this type of analysis allows us to identify "race condition" vulnerabilities that are undetectable even under aggressive testing conditions under maximum loads.

The Future of Artificial Intelligence: Moving from Error Detection to Self-Healing Code

The most promising paradigm shift is to move away from error detection to self -healing code.

Today, experimental AI systems are capable of:

offer contextually correct patches rather than general recommendations;
check if the fix breaks other parts of the logic;
learn and improve from the history of changes of a specific project.

The world of modern technology is changing rapidly, and today's software code is not simply tested, but undergoes regular checks to adapt, like a living system.

Conclusion

Synthetic intelligence is changing the very concept of security in the software development lifecycle. It is no longer just a tool for finding bugs, but a cognitive component of the entire system that understands how and why all processes occur, including failures.

In a technological world where software products are rapidly becoming more complex, logical security is becoming a key guarantee of reliability. Artificial intelligence is not just an optional extra for convenience, but an infrastructural necessity.

Synthetic intelligence is changing the approach to security in the software development life cycle (SDLC). Instead of searching for individual errors, it analyzes the entire behavioral model of the system. This allows for the identification of hidden abuse scenarios even before the code enters production.

The integration of AI tools complements security systems with preventative engineering controls. In the near future, we will witness a shift from simply detecting logical vulnerabilities to autonomous code remediation, where systems will be able not only to detect errors but also to independently restore the integrity of logic.