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Introduction:
The application security (AppSec) landscape is undergoing a fundamental shift, moving from complex, hard-coded security rules to dynamic, intelligent enforcement driven by natural language. By leveraging large language models (LLMs), security teams can now articulate security requirements in plain English, which are then automatically converted into actionable, context-aware policies that scan every pull request. This evolution from static analysis to an “agentic” AppSec engine promises to dramatically scale security expertise and close the gap between developer velocity and security coverage.
Learning Objectives:
- Understand the core architecture and components of a natural language-driven AppSec engine.
- Learn how to formulate effective natural language policies for common security and compliance checks.
- Gain practical skills for integrating and validating these agentic systems within CI/CD pipelines.
You Should Know:
1. Core Architecture: The Agentic AppSec Engine
The system transforms a simple query into a security action through a multi-step process. It’s not a single prompt but a pipeline of specialized agents.
Example Policy Configuration (dryrun.security.blog) policy_name: "Prevent_Username_Enumeration" natural_language_prompt: "Does this change introduce username enumeration vulnerability?" trigger: - on_pull_request - on_direct_push agents: - code_change_analyzer - vulnerability_reasoning_engine - security_enforcement_gate
Step-by-step guide:
This YAML structure defines a policy in an agentic system. The `natural_language_prompt` is the human-readable input. Upon a code change trigger, the request is routed to a `code_change_analyzer` agent, which dissects the Git diff. The output is passed to a `vulnerability_reasoning_engine` (often an LLM) that evaluates the changes against the prompt’s intent. Finally, a `security_enforcement_gate` agent makes a pass/fail decision and comments on the PR. The power lies in the orchestration of these discrete, intelligent agents.
2. Crafting Effective Natural Language Policies
The precision of your natural language input directly correlates with the accuracy of the security scan. Vague statements produce unreliable results.
Effective: "Do any libraries introduced in this PR violate our internal licensing requirements?" Ineffective: "Check for license problems."
Step-by-step guide:
To create a robust policy, be specific about the scope (libraries introduced), the object of concern (internal licensing requirements), and the condition (violate). The system uses these semantic markers to understand which part of the codebase to analyze (dependencies/files), what to look for (license text in pom.xml, package.json, etc.), and what constitutes a violation. Start with a narrow, well-defined policy like the example above before moving to broader concepts.
3. Enforcing API Security with Natural Language
A common critical use case is ensuring new API endpoints are properly secured from the start.
Natural Language Policy: “Does this code introduce a new API endpoint without authorization enforcement?”
Example Git command to test the policy scope git diff origin/main...HEAD --name-only | grep -E '.(java|py|js|ts|go)$'
Step-by-step guide:
This policy would trigger an agent that first identifies new API endpoints by scanning for changes in route annotations (e.g., @PostMapping, @app.route) or new controller files. Then, a second agent analyzes the code blocks containing these routes, looking for the presence of authorization checks like @PreAuthorize, auth.check(), or specific role-based conditionals. If an endpoint is found without such a check, the policy fails and blocks the merge.
- Implementing a Software Bill of Materials (SBOM) Check
Proactively controlling open-source risk is a perfect application for this technology.
Natural Language Policy: “Scan all introduced and updated dependencies for CVEs with a severity of ‘Critical’.”
Manual equivalent using OWASP Dependency-Check dependency-check.sh --project "MyApp" --scan ./path/to/src --out ./reports Then, parse the report for CVSS >= 9.0 grep -i "CRITICAL" ./reports/dependency-check-report.html
Step-by-step guide:
The automated agent performs a similar but integrated workflow. It generates a differential Software Bill of Materials (SBOM) for the PR using a tool like Syft. It then feeds this list of new/updated packages into a vulnerability scanner (e.g., Grype, Trivy). The agent’s logic filters the results based on your policy’s criteria (e.g., severity == Critical). If any matches are found, it fails the build and reports the vulnerable packages and CVE IDs directly in the PR comment.
5. Hardening Cloud Infrastructure as Code (IaC)
Security shifts left by applying these policies to infrastructure code like Terraform or CloudFormation.
Natural Language Policy: “Do any new AWS S3 buckets defined in this Terraform code have ‘public-read’ access enabled?”
Example of a NON-COMPLIANT Terraform resource
resource "aws_s3_bucket" "data_lake" {
bucket = "my-public-data-lake"
This would cause the policy to fail
acl = "public-read"
}
Step-by-step guide:
The agentic engine parses the IaC files in the pull request. It identifies all `aws_s3_bucket` resources (or their CloudFormation equivalents) and inspects their configuration blocks. It specifically looks for the `acl` attribute set to public-read/public-read-write or a `bucket_policy` that grants "Principal": "". Discovering such a configuration triggers a policy violation, preventing a misconfigured, public-facing storage bucket from being deployed.
6. Validating Secret Detection and Prevention
While traditional secret scanners have high false positives, a natural language agent can provide context-aware validation.
Natural Language Policy: “If a potential hardcoded secret is detected, verify it is a false positive by checking if it’s in a test file or a placeholder value.”
Example Python snippet a basic scanner might flag API_KEY = "sk_test_123456789" Fake key for local testing
Step-by-step guide:
This creates a two-stage validation process. First, a primary secret-scanning agent (e.g., Gitleaks) runs and flags potential secrets. Instead of stopping there, a secondary, reasoning agent analyzes the context of the finding. It checks the file path (/test/ vs. /src/), looks for comments indicating a placeholder (Fake key, Example), and assesses the value itself (e.g., does it match a known test pattern?). This drastically reduces noise and allows developers to focus on genuine secrets in production code.
7. Custom Compliance and Business Logic Rules
This approach excels at enforcing rules that are unique to an organization’s compliance needs.
Natural Language Policy: “Ensure all new database connection strings use encrypted channels and do not point to legacy, unencrypted database clusters.”
-- This connection string in a config file would fail the policy "Database=myDB;Server=legacy-db-prod.internal.com;User Id=myUser;" -- This one would pass "Database=myDB;Server=new-db-prod.internal.com;Encrypt=True;"
Step-by-step guide:
The agent is trained to recognize connection strings or database configuration blocks within application code or config files (e.g., application.properties, `.env` files). It performs a pattern match to identify the server endpoint. If the endpoint matches a known list of non-compliant, legacy servers (e.g., legacy--db-), it fails. Concurrently, it checks for the presence of encryption flags like Encrypt=True, ssl=require, or useSSL=true. The absence of these flags also results in a policy failure.
What Undercode Say:
- Democratization of Security: The primary takeaway is the democratization of advanced security controls. Security experts are no longer the only ones who can write and deploy security rules; developers and product managers can now articulate logic in their own language.
- The Rise of Context-Aware Analysis: The shift from regex-based pattern matching to semantic, context-aware reasoning is a quantum leap. It moves AppSec from a noisy, obstructionist gatekeeper to an intelligent, collaborative partner in the development process.
This technology represents more than an incremental improvement; it’s a foundational change in the AppSec workflow. By using natural language as the interface, it bridges the communication gap between security intent and developer action. The examples provided by DryRun Security—checking for license compliance, username enumeration, and API authorization—are not futuristic concepts but operational realities. The analysis suggests that the “agentic” model, where multiple, specialized AI agents work in concert, is key to scalability and accuracy. This approach effectively codifies the tacit knowledge of senior security engineers, allowing it to be applied consistently at scale, turning every pull request into a teachable moment and a enforcement point without overwhelming the development team.
Prediction:
The widespread adoption of natural language, agentic AppSec engines will fundamentally reshape software development roles within two years. Security engineers will transition from writing granular rules to curating and training sophisticated policy agents, becoming “security architects” for AI-driven systems. For developers, this will embed expert-level security guidance directly into their workflow, making secure coding the default path and drastically reducing the prevalence of common vulnerabilities like broken access control and cryptographic failures. This will, in turn, force a shift in the attacker landscape, as low-hanging fruit disappears, compelling adversaries to focus on more complex logic flaws and software supply chain attacks.
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IT/Security Reporter URL:
Reported By: Cktricky Placed – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
