Application security has operated on human timelines for as long as it's existed: a scanner flags something, a ticket gets filed, developers and security argue over priority, and a fix ships weeks or months later, if it ships at all. Rahul Sood, GM of Application Security at Harness, points to Log4j as the cautionary tale everyone in the industry already knows by heart. "It was not uncommon for companies to say it took us almost one year to get rid of it," he said. "Right now, that does not work when attackers have AI that can identify vulnerabilities in your code, even if you thought your code was safe."
Harness is launching a set of AI-driven application security capabilities this week aimed at collapsing that timeline. The stakes are concrete: attackers are now going from disclosure to first exploit in as little as six hours, while the average vulnerability still takes more than 50 days to fix. Sood breaks into three required shifts: defenders need to actually use AI to find weaknesses in their own code, the window between discovering a vulnerability and fixing it needs to compress from months to hours, and organizations need to accept that not every fix will land before an attacker finds a way in, which means building a containment strategy alongside the fix itself.
Scanning with frontier models is real, and also too slow to run daily
Sood doesn't dispute that frontier models can scan code effectively. The problem is cost and speed. He cited a bank going through Anthropic's Glasswing program that took three months to scan its entire codebase using an LLM — and Glasswing partners broadly have surfaced roughly 10 times more vulnerabilities using LLM-based scanning than traditional tools find, a wave of visibility that just becomes a bigger backlog without a faster way to act. "The cost is it takes a lot of tokens. The output is not deterministic, and it's not easy for you to go and triage these vulnerabilities," he said.
Harness's answer is two separate capabilities rather than one. The first, LLM Scan Orchestration, is a native integration between its CI/CD security tooling and LLM-based scanning, supporting both Claude and OpenAI's Codex, so findings get triaged, deduplicated, and normalized into a developer's existing workflow instead of arriving as an unstructured wall of output. The second, AI SAST, layers AI reasoning on top of traditional static analysis, aiming to catch what conventional scanners miss (Sood pointed to business logic attacks as an example) while using that reasoning to cut false positives and explain why a finding matters. From there, a Triage Agent automatically prioritizes what's actually exploitable so teams can focus on real risk, and a Remediation Agent writes and validates a fix for a prioritized finding, opening a pull request for a developer to review and approve.
A zero-day agent that maps blast radius across your entire environment
For open-source and third-party code specifically, Harness is launching an agent that tracks newly disclosed zero-days and maps them against a customer's actual environment: artifacts, pipelines, and repos. Sood argues this is where Harness's position gives it an edge most competitors can't match, since the platform already has visibility into what's in production and what's being built. Once the blast radius is identified, policies can block new builds that carry the vulnerability while triage and remediation catch up. Sood's claim is that this compresses zero-day response from weeks or months down to hours.
The differentiator he leans on hardest, though, isn't detection. It's remediation that's actually been checked. "We don't just validate that the security vulnerability is being fixed. We also validate that the fix will not break your pipeline," he said. Because Harness manages the pipeline itself, a proposed fix gets tested against the customer's own unit tests before it ships, not just checked against the vulnerability in isolation.
Closing the loop with production shielding
Even with a compressed timeline, not every fix lands before an attacker moves. Harness's answer is a virtual patch at the perimeter, deployed against a specific API to block the traffic pattern that would exploit a known vulnerability. Customers choose between two modes: monitoring, where the patch alerts a human to assess, or blocking, where the traffic is stopped automatically. Sood said the choice comes down to a customer's confidence level, the criticality of the API, and their overall risk appetite, rather than a one-size-fits-all default.
That same logic extends to how much autonomy the underlying agents get generally. Sood expects most customers to start with a human in the loop and move toward more autonomous workflows only as trust builds, and the platform supports the full range, from fully manual to fully autonomous, configurable per customer and even per class of fix.
What changes for the developer staring at a vulnerability list
For engineers used to a scanner dumping a long, mostly-noise list of findings, Sood said the real shift isn't just fewer false positives, it's clarity on what's actually reachable and exploitable versus theoretical. Combined with a recommended fix that's been validated against a team's own test suite, the goal is to give developers back the time currently lost triaging vulnerabilities that were never going to matter. "Development teams are being completely overwhelmed by the huge number of vulnerabilities being generated by scanners," Sood said, to the point where it crowds out shipping actual business-relevant code.
What to be skeptical of
Asked what's overhyped in this space even as he's building in it, Sood didn't hedge. "This entire claim that agents can do all this work for you," he said, pointing specifically at remediation. "Most of it doesn't really work." His advice for developers and architects evaluating any vendor's AI remediation claims, Harness included: test it yourself, and be skeptical of a fix recommendation generated without real understanding of your specific environment and test cases. A recommendation that isn't validated against your actual code is still just a guess wearing a more confident outfit.