The platform gives security teams one environment for coordinating a rapidly expanding field of AI-powered security systems.
NEW YORK, August 18, 2026 — Security teams are gaining access to more capable AI security systems at a pace that is beginning to create a new operational challenge.
Web3 Security Company Sherlock is betting that orchestration will become increasingly important as that field expands.
The company today launched Sherlock Audit Engine, a security auditing platform that coordinates frontier language models, purpose-built AI auditors and AI-enabled security researchers inside a single review.
An engineering team can already experiment with several frontier models, contract specialized AI auditors and maintain internal security agents of its own. Each system can add another perspective on the code, while also generating another stream of findings requiring evaluation.
Audit Engine is designed to bring those streams together.
The Bottleneck Is Shifting
The same transition is becoming visible across the broader application security market.
In March, OpenAI introduced Codex Security, an application security agent designed to identify higher-confidence vulnerabilities while reducing the low-impact findings and false positives that create manual triage work for security teams.
By June, OpenAI’s broader Daybreak cybersecurity initiative was describing a market where frontier models can navigate increasingly large codebases and surface vulnerabilities faster, moving more of the defensive burden toward validating, prioritizing and remediating what those systems find.
For security teams, that shift changes the problem.
As vulnerability discovery becomes easier to scale, determining which findings are valid, where multiple systems are reporting the same issue and which approaches are actually contributing additional coverage becomes increasingly important.
Audit Engine is built around that layer.
Sherlock runs multiple security approaches against the same scoped code, then handles judging, validation, clustering, deduplication and synthesis across the engagement. The resulting security signal is consolidated into one final audit result.
The platform also measures how the participating approaches performed.
Coverage, precision and incremental contribution give teams a codebase-specific view of which systems generated useful security signal and where their capabilities overlapped.
That information can influence future review composition, security spending and internal tooling decisions.
Sherlock is positioning Audit Engine as the orchestration layer for AI-native security review, built for a market where the number and capabilities of security systems are likely to keep expanding.
Building for the Next Security Stack
Audit Engine reflects a broader direction Sherlock has been building toward across its security platform.
Sherlock is a smart contract auditing firm that works with onchain teams across the security lifecycle, from securing code during development to high-intensity auditing before deployment and ongoing scrutiny of live systems through bug bounties. As AI becomes more capable across each of those stages, Sherlock expects security teams to rely on an increasingly diverse mix of models, specialized systems and human expertise.
That creates a different kind of infrastructure problem. The advantage will come from determining how those systems should work together, measuring what each contributes and turning their combined output into decisions engineering teams can act on.
Sherlock sees Audit Engine as an early step toward that future: a security environment capable of continuously incorporating stronger models, new AI auditors and new approaches as they emerge, without requiring teams to rebuild their security process around every new tool.
MEDIA CONTACT
Alec Novella
Head of Marketing, Sherlock
New York City, United States
alec@sherlock.xyz
