Deep Remediation: you deserve much more than a small diff of code

In many “AI remediation” tools, artificial intelligence is reduced to a slightly more sophisticated autocomplete. It suggests a local patch that is sometimes plausible, often fragile, and almost never aligned with the real complexity of a modern codebase.

With Deep Remediation, the ambition is different: helping teams move from a “line‑by‑line patching” mindset to an agentic, contextualized approach, capable of reasoning across multiple files and proposing fixes that last.

The problem: when “fixing a vulnerability” is no longer enough

In mature organizations, detection is no longer the main bottleneck. SAST and IaC scanners and CI/CD pipelines already surface huge volumes of alerts. What blocks progress is remediation:

• Vulnerabilities are reintroduced a few weeks after being “fixed”, because developers do not have a deep understanding of the issue.
• Fixes are often local, without taking into account dependencies or data flows across the codebase.
• AppSec teams spend a significant amount of time re‑reading, correcting, and requalifying remediations suggested by tools that are blind to context.
• Current AI tools sometimes only look like they understand: they modify a file, but without checking that the build passes, that tests still hold, or that the overall architecture remains coherent.

The result:

we talk a lot about “shift left”, but in practice we often just shift the problem onto developers, without giving them the right levers to remediate cleanly and sustainably.

Deep Remediation: an agent that reasons, plans and verifies

Deep Remediation was designed to tackle this problem head‑on. Instead of a simple model that proposes a code snippet, it is a full agentic workflow.

First, an honest framing:

Deep Remediation is not (yet) an agent that autonomously drains your backlog. It is a deeply guided remediation capability that teams trigger and steer, but which takes over a large part of the analysis, planning, and proposal of fixes.

1. Deep analysis of the vulnerability
   ‍The agent starts by reconstructing the real context of the problem:
   • directly affected files;
   • upstream and downstream calls;
   • data flowing between functions, modules, and even services.
   The goal is not just to “make the alert disappear”, but to understand where the real flaw is and which components it affects.
2. Multi‑file planning
   ‍Deep Remediation is not limited to a single file open in the IDE. It can:
   • load related files (routes, controllers, services, configs, tests);
   • build a multi‑step remediation plan that covers all required changes;
   • order these changes to avoid inconsistent intermediate states.
3. Execution with full CRUD capabilities
   ‍The agent can:
   • create new files (tests, helpers, secure configs);
   • read and interpret existing code;
   • update several files in a coherent way;
   • delete dead code or dangerous patterns when necessary.
   Each action is guided by the initial plan and can be adjusted depending on what the agent discovers along the way.)
4. Verification and internal feedback loop and once changes are applied, Deep Remediation does not stop there:
   • it checks that the fix does not break the build;
   • it can rely on existing tests to detect obvious regressions;
   • it re‑evaluates the initial vulnerability to ensure the root cause is addressed, not just the visible symptoms.
   This “reasoning → action → verification → adjustment” loop is repeated until the agent is satisfied with the produced fix according to predefined criteria.

The power of context: real understanding, not pattern matching

What truly differentiates Deep Remediation from most current AI approaches is how it uses context.

• Multi‑file contextThe agent sees not only the vulnerable file, but also:
  • internal security libraries,
  • authentication helpers,
  • logging conventions,
  • configuration modules.
  It can, for example, decide to centralize a fix in a middleware rather than duplicating a fragile patch across multiple API handlers.
• Alignment with specific patterns
• Deep Remediation adapts to team‑specific conventions through instruction files that align with coding practices and specific rules. This moves away from generic copy‑paste snippets and towards fixes that truly look like something a senior engineer on the team would have written.
• Memory of past vulnerabilities

‍When coupled with existing AI remediation systems, the agent can capitalize on validated fixes to avoid repeating the same mistakes:

• if it encounters a known pattern, it will reuse a remediation approach that has already proven effective;
• it helps standardize the security posture across the entire codebase.

Accelerating remediation without breaking developer flow

One of the most impactful benefits of Deep Remediation is its ability to speed up the treatment of security debt without overwhelming teams.

• Teams can launch the agent on a set of prioritized vulnerabilities (historical backlog, critical findings, accumulated debt).
• Deep Remediation proposes complete remediations that are coherent with the existing architecture, while respecting the environment constraints (stack, internal security rules, CI/CD).
• Developers receive pre‑filled MRs/PRs with:
  • a readable, contextualized diff focused on security‑relevant changes;
  • fixes that are robust enough for the review to take seconds instead of tens of minutes per vulnerability.

In other words: security keeps moving forward without blocking the development flow or draining AppSec teams.

"In most of the teams, Deep Remediation has 5× the remediation speed, with marginal overhead for the developer."

The missing piece: just‑in‑time learning on remediation

Automated fixes alone are not enough. If developers approve changes without understanding their implications, the chances of reintroducing the same vulnerability in the medium term remain high.

This is where the coaching and micro‑training layer comes in.

• For each remediation reviewed or accepted by developers, Deep Remediation can trigger a 3**‑minute micro‑training** that explains:
  • what was fixed;
  • why the original code was vulnerable;
  • how to avoid reproducing the same pattern.
• This content is deeply contextualized:
  • it is based on the type of vulnerability that was introduced;
  • it uses the same language that led to the vulnerability in the first place.
• Assessment and PR review exercises reinforce learning by:
  • generating interactive quizzes tied to actual pull requests;
  • asking developers to identify security issues in code similar to their changes;
  • validating understanding before code is merged.

The goal is not just to address the consequences (risk):

we treat the root cause, by upskilling teams where vulnerabilities are actually created.

Deep Remediation: towards truly agentic security

Development workflows are evolving towards vibe coding and agentic dev environments. Security tools need to follow the same path: they can no longer just analyze and report. They must reason, plan, act, and verify.

Deep Remediation fits squarely into this trajectory:

An agent dedicated to remediation, capable of handling the real complexity of modern codebases, significantly reducing security debt, and turning every fix into an opportunity for team growth.

That may be the real promise of security in the age of AI:

not just fixing faster, but remediating more intelligently, in symbiosis with the people who build the software.

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