The launch of AI-powered security tools like Mythos could dramatically lower the cost of smart contract audits and enable continuous code reviews, potentially changing what the crypto industry considers reasonable security due diligence. According to reports from CoinDesk, Alexander Urbelis, chief information security officer at ENS Labs, stated that "It pushes the price of a basic audit toward zero." Work that once required weeks and significant expense could eventually be completed in minutes, allowing projects that previously could not afford professional reviews to obtain fast security assessments.
While AI systems like Mythos take a different approach than traditional automated tools, they represent a significant advancement in security analysis. As reported by CoinDesk, David Schwed, COO of blockchain security firm SVRN, described the shift as "These models now operate the way a human attacker does." The older tooling was just complicated deterministic flows, but now AI systems can "iterate, they take the next step based on what they're seeing in real time." Rather than simply identifying technical bugs, systems like Mythos can "infer what code was intended to do and compare that against what it actually does."
The most significant change may be the emergence of continuous security monitoring with suggested remediations at a fraction of the cost. According to CoinDesk, Schwed emphasized that "The real shift is continuous auditing with suggested remediations at a fraction of the cost, instead of a point-in-time review you can only afford once." This capability could significantly expand the industry's ability to identify vulnerabilities before launch, particularly valuable given that smart contract code is public and bug bounties can have substantial budgets.
The integration of AI tools into secure development requires a comprehensive framework that treats AI as one control in a layered review process. According to Netizen, secure code review should include five distinct gates: developer-side review before pull requests, automated security analysis, AI-assisted review, human review for high-risk areas, and post-merge monitoring. Teams must define which code paths require human security review and update pull request templates to surface AI usage in high-risk changes. Security teams should build small, precise review rules that map to known internal failure modes, such as "No new cloud role may include wildcard resource access without a linked exception" or "All API handlers that load objects by ID must call the tenant authorization helper before returning data."
The most critical challenge in AI-assisted code review is responsibility drift, where developers may assume AI caught security issues or security teams may assume developers reviewed AI output. As reported by Netizen, "No one may own the final security judgment." The process must clearly assign responsibility: developers remain responsible for code they submit, human reviewers own approval decisions, and security teams manage standards, tooling, and high-risk review paths. AI-generated comments should be treated like SAST findings, not final judgment, and every generated security fix should be reviewed as a security change. Pull request templates should ask whether AI was used for security-sensitive code, whether generated code was modified, and whether negative security tests were included.
