The Transformation of Smart Contract Audits Through AI

The Transformation of Smart Contract Audits Through AI

Opinion by: Jesus Rodriguez, co-founder of Sentora

Artificial Intelligence (AI) is poised to revolutionize the process of smart contract audits in the Web3 domain. Current auditing methods rely on static, one-off evaluations that may not fully account for the complexities of a rapidly evolving blockchain environment. By integrating AI, audits can become continuous, thorough, and adaptive, keeping pace with an increasingly composable and adversarial digital landscape.

Key Takeaways

AI’s Impact: AI is set to change smart contract audits from episodic checks to continuous assessments.
Traditional Audit Limitations: Current audits are not frequent enough and often fail to capture real-time vulnerabilities.
Smart Contract Complexity: The intricacies of smart contracts make traditional auditing approaches inadequate.
Future of Audits: A combination of AI and comprehensive evaluation methods will become essential for effective auditing in the smart contract space.

The Need for Change in Audits

Audits have become a standard practice in Web3, serving as crucial evidence that teams have attempted to secure their systems before they launch. However, this practice is reminiscent of an older development era. In conventional software development, assurance is woven directly into the workflow. This includes tests, continuous integration/continuous deployment (CI/CD), and dynamic analysis. In contrast, Web3 has resurrected the milestone-based audit process due to the unique challenges of immutability and adversarial economic conditions. The logical next step is to incorporate AI into auditing practices, allowing for continuous assurance rather than a single assessment.

Recognizing Audit Limitations

While audits provide valuable insights and breathing room for teams, they have their inherent limitations. They compel teams to define and test essential invariants, assumptions, and potential failure modes before deploying capital. A thorough audit ideally leaves behind useful artifacts, such as threat models and regression tests, that adapt alongside the smart contract as it evolves.

However, audits can become outdated quickly due to rapid changes in technology, liquidity, governance actions, and market strategies. Because of this, reports can inadvertently give teams a false sense of security. The biggest risks often stem from economic factors rather than coding errors, necessitating approaches like simulation and real-time monitoring.

AI's Current Challenges in Coding Smart Contracts

Currently, AI demonstrates impressive capabilities in data-rich environments, yet it struggles with the nuances of smart contract coding. Success in programming languages depends not just on correctness but on how code interacts with adversarial actors. Specific aspects like execution order and potential attack vectors (including reentrancy and front-running) are critical in languages such as Solidity. Furthermore, certain aspects of smart contracts are not readily available in training datasets, making them hard to capture using traditional testing methods. Improvement in this area is achievable through enhanced data gathering, clearer labels, and tool-assisted feedback.

Developing AI Auditors: A Practical Approach

To effectively leverage AI in smart contract auditing, three essential components are needed:

Innovative Audit Models: These would combine large language models with symbolic reasoning and simulation backends. The goal is to have models that can extract intent, suggest invariants, and generalize from examples while grounded in proven patterns.
Functional Agent Processes: These consist of specialized agents, such as property miners and dependency crawlers, which assess risks across platforms. An orchestration of agents could continuously monitor, reason, and act to enhance security.
Effective Evaluations: Measurement must go beyond basic unit tests. New metrics could include tracking coverage, detecting economic failures, and evaluating response times. Assurance should become a service offering clear performance metrics for insurers and governance bodies.

The Role of Generalist AI Auditors

While a hybrid approach combining specialists is promising, emerging trends indicate that generalist AI models, capable of managing entire processes, may outstrip segmented systems. A powerful model could potentially grasp security principles, maintain long-term memory, and integrate all necessary functions into a unified framework.

The Inevitable Rise of AI in Smart Contract Auditing

With the nature of Web3 inherently adversarial and constantly changing, traditional, static audits may not suffice. AI is well-positioned to thrive in environments where code, feedback, and verification processes are abundant. Whether tomorrow’s auditing method relies on the current hybrid model or a future generalist approach, the trend is clear: auditing must evolve from a milestone-centric process to a continuous, AI-driven function, bolstered by proofs and real-time monitoring.

View audits not just as tasks, but as integral products, initiating a continuous improvement cycle. Investing in AI-enhanced assurance strategies will transition the auditing process into a robust operational capability necessary in today’s complex digital ecosystem.

Opinion by: Jesus Rodriguez, co-founder of Sentora.