Part Two - When AI Writes the Code, Who Fixes the Bugs? What CISOs Should Do Now: A Practical 5-Step Plan
AI now writes a massive slice of your codebase. Without an agentic remediation layer, it can also quietly rewrite your risk profile.
Treat 2026 as the year you move from exploration to execution. In part one of this blog, we discussed how agentic remediation doesn’t live in a vacuum. It’s most formidable when it’s part of end-to-end AI governance and security.
Here’s a 5-step practical plan that every CISO should already be implementing:
1. Discover Where AI Already Writes Your Code
- Inventory AI coding assistants in use (Copilot, CodeWhisperer, Windsurf, Cursor, etc.).
- Deploy discovery to tag AI-generated commits (AI-BOM/PBOM-style).
- Set baseline policies: which tools are approved, where, and under what conditions.
2. Measure the Remediation Gap
- Compare MTTR for AI-generated vs. human-authored code.
- Survey developers: “How confident are you in fixing vulnerabilities in code you didn’t write?”
- Quantify false positives and reopened tickets linked specifically to AI-authored logic.
3. Pilot Agentic Remediation in Low-Risk Systems
- Pick a non-critical app with known AI-generated hot spots.
- Start with semi-autonomous PRs that still require human review.
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Evaluate vendors on:
- Successful fix rate without regression
- Clarity of explanations in PRs
- Integration friction with your CI/CD stack
4. Enforce a Rigorous Validation Framework
Require AI-driven fixes to pass:
- SAST / SCA
- Unit & integration tests
- Security fuzzing or targeted adversarial tests
- Define explicit rollback and monitoring playbooks if something slips into production.
- Periodically audit AI rationales for quality and consistency.
5. Scale Deliberately with Guardrails
- Expand from pilot → business unit → high-value systems.
- Increase autonomy only where risk is low and validation is strong.
- Train developers and AppSec teams to interpret, override, and tune agentic fixes rather than blindly accept them.
How to Choose a Platform: Capabilities, Questions, and Red Flags
When everyone claims “agentic remediation,” you need sharper filters.
Core Capabilities to Look For
- AI Code Discovery: AI-generated code detection across repos; AI-BOM/PBOM support; ability to distinguish tools (Copilot vs. CodeWhisperer, etc.).
- Remediation Autonomy: Guided, semi-autonomous, and fully autonomous modes; configurable by app or vuln type; recursive validation and self-correction.
- Code-to-Cloud Correlation: Links code-level findings to runtime: Is it deployed? Exposed? Handling sensitive data? Prioritization by exploitability and business impact.
- Explainability: Operational rationales for each fix; clear, auditable explanations; easy human override.
- Validation Rigor: Multi-layer validation (static, dynamic, fuzzing); behavior on failure (retry vs. escalate); customizable per app/risk class.
Smart Vendor Questions
- How does your PBOM/AI-BOM track AI-generated code through CI/CD?
- What cryptographic standards (e.g., SHA-256, RSA-2048) protect the integrity of those records?
- Can you show MTTR reduction specifically for AI-generated code?
- Do you have published case studies with quantifiable results?
Red Flags
- No robust validation framework—just “we generate fixes.”
- Hand-wavy explainability: can’t really show why a change was made.
- Overstated “fully autonomous” claims with no guardrail story.
- No code-to-cloud mapping; teams end up fixing theoretical issues while real exposure remains.
Looking Ahead: From Guided Automation to True AI Co-Defense
In the short term, agentic remediation will go mainstream as enterprises realize detection alone cannot keep pace with AI-generated code.
In the medium term, we’ll see semi-autonomous security operations: AI agents not only patch vulnerabilities but also generate audit trails and predict where failures will appear next.
Long term, development becomes AI-native by default:
- AI assistants suggest code.
- Security agents validate and fix in-line.
- Humans handle strategy, high-risk changes, and policy.
Throughout that evolution, one principle doesn’t change:
If you can’t explain how a vulnerability was fixed, you can’t honestly say it was governed.
Agentic remediation, backed by platforms like Cranium, gives you both speed and assurance—innovation and accountability. AI-generated code is rewriting how software is built. Your job now is to make sure it doesn’t quietly rewrite how security fails.
Bottom Line
- Agentic remediation isn’t the enemy of developer speed; it’s how the fastest teams ship safely when AI is writing half the code.
- The gap isn’t just between teams that use AI and teams that don’t. It’s between organizations that prove how AI-generated code is discovered, validated, and fixed, and those that just hope their scanners keep up. That’s the control layer Cranium helps you build.
Explore how Cranium gives your team a head start in governing AI-generated code, reducing MTTR, and turning AI into a co-defender across your code-to-cloud stack: https://cranium.ai
