When Static Rules Met a Dynamic Attack Surface: Why AI Coding Assistants Must Think Like the AI Era – Not Like 80s Firewalls

In the early days of network security, perimeter defense was simple:

Inspect packets. Match them against a list of known bad patterns. Block anything that looked suspicious.

That was the era of static firewall rules – a world where threats were relatively predictable, environments were mostly stable, and thick rulebooks somehow worked.

Then came the Next-Generation Firewall (NGFW) revolution.

Innovators like Palo Alto Networks made it painfully clear that static rules could not keep up with modern reality: dynamic web attacks, encrypted traffic, constantly shifting flows. Security had to evolve. It needed context, awareness, and adaptability – not rigid, brittle rule sets.

Fast forward to modern software development.

We are repeating the same mistake.

From SAST Noise to AI Speed – And Back to the 80s

Application security started with SAST rules. The result? Endless noise, false positives, and alert fatigue – creating friction between developers and AppSec teams, slowing down releases, and ultimately impacting business growth.

🔗 More can be found here re: the issues with traditional SAST tooling

Then AI coding assistants entered the picture – and everything accelerated. Development velocity increased 4×. Risk expanded 10×.

Yet instead of rethinking application security for this new era, many AI coding assistant vendors (Cursor, GitHub Copilot, Gemini Code Assistant) went back in time – reintroducing static, rules-based controls, now branded as “developer-defined guidelines.”

These rules were originally designed for a narrow, reasonable purpose: to give AI assistants project-specific context not present in their general training data.

Their goals were pragmatic:

• Enforce coding standards (naming conventions, libraries, structure)
• Improve AI output quality by nudging preferred patterns
• Encode basic best practices for security, performance, and error handling

All good intentions.

But what started as guidance is now being stretched into security use cases.

And that’s where the fallacy begins.

The Static Rules Fallacy in AI Coding Assistants

Here are 3 examples of static security rules:

• “No hardcoded secrets”
• “Implement input validation”
• “Encrypt PII when written to logs”

They all make sense. Just like firewall rules blocking “bad IPs.” 

But modern software development looks nothing like the static environments of the 1980s.

Today, AI coding assistants reshape the software architecture graph and expand your software attack surface for every code generated by:

✅ Adding duplicated and/or low reputation OSS dependencies and/or unvetted technologies

✅ Introducing new business logic vulnerabilities (e.g., authorization flows)

✅ Developing unnecessary APIs, using different frameworks with new patterns

In short: your software changes faster than static rules can keep up.

Why Static Rules Inevitably Break Down

Think back to legacy firewalls. How often did a new app, a shifted port, or encrypted traffic instantly invalidate your carefully crafted rules?

Static coding rules face the same fate:

🚫 They lack software architectural graph context (e.g., APIs, languages, OSS dependencies, authentication/authorization frameworks)

🚫 They can’t connect code changes to the application’s business impact (e.g., regulatory requirements, deployment locations, revenue generation)

🚫 They ignore organizational policies, runtime behavior, 3rd-party services and exit points

Just as static firewall rules failed against dynamic attacks, static coding rules fail to prevent dynamic software risk.

​​The AI Era Demands Adaptive Software Intelligence

To avoid repeating the mistakes of the past, AI coding assistants must evolve beyond static rules. They need context from the software graph – from code to runtime, organizational policies, and connectivity to achieve modern software security at scale.

Instead of rigid rules, they must be able to:

🔍 Understand the Software Graph

Not just syntax, but the living graph of software resources – from code to runtime: code modules, services, APIs, data models, sensitive data flows, open source dependencies, internal packages, authentication and authorization frameworks – and how they connect and where they are deployed.

🧠 Tie Changes to Business Impact

A line of code itself isn’t risky because attackers don’t target “lines of code.” Risk emerges from the software resources and how they connect across the software graph – what is the nature of the code change, where it deploys/runs, and what it touches: PII, authentication paths, vulnerable OSS dependencies, and sensitive business logic. Together, these connections define your software attack surface.

📊 Map Risks Across the Software Graph

Every resource in your software graph evolves – just like your policies, compliance requirements and the business impact of your application. Some resources – like OSS dependencies – change outside your control and might contain 0-days or malware – yet fundamentally shape your attack surface and application risk profile. Static rules can’t see this. Dynamic models can.

⚡ Adapt in Real Time

Every feature request, product spec, prompt, commit, PR, release, and dependency update reshapes the landscape. Static rules freeze. Adaptive software intelligence evolves.

From Rules to Guardianship

Static rules had their moment – in a simpler era.

But just as NGFWs made legacy firewall rulebooks obsolete, AI coding assistants must evolve beyond checklist thinking.

Developers don’t need more rules. They need guardianship.

A guardian AI Agent that dynamically analyzes the prompt to understand intent, then queries the software graph and risk graph to apply software architectural context – including code to runtime and organizational policy – can transform easily-sidestepped static rules into intelligent, security-first insights:

From → Static rules	To → Dynamic, contextual insights
“No hardcoded secrets”	Ask context-aware questions such as: 1. Does this repository/application already use a Key Management Service (KMS)? 2. If yes, which KMS is approved (e.g., HashiCorp Vault, AWS KMS, GCP KMS)? Which framework and version? 3. Are there existing code patterns that already passed security and compliance checks? 4. What regulatory or policy requirements apply to this repository/application? (PCI DSS v4, GDPR / PII handling, Internal secure coding standards) Then, instead of static rule, generate a secure prompt, for example: “Use HashiCorp Vault with the .NET client version 1.17.5, as approved for this repository/application.” “Here is a contextual code from a different code module that passed all security and compliance checks and matches your architecture.”
“Implement input validation”	Ask context-aware questions such as: 1. Does this repository/application already use Input Validation frameworks? 2. If yes, which Input Validation is approved (e.g., Sprint Security, ASP.NET Core Model Validation, Pydantic)? Which version? 3. What type of input is being processed? (PII, payment data, PHI data, auth tokens, etc.) 4. Which input validation frameworks and patterns are already approved and in use in this repository/application? 5. What risks apply to this input path? (Injection SQL, NoSQL, command, template, XSS, Deserialization, Prompt injection for AI-facing endpoints) 6. What regulatory or policy requirements apply to this repository/application? (PCI DSS v4, GDPR / PII handling, Internal secure coding standards) Then, instead of static rule, generate a secure prompt, for example: “This API is internet-facing and processes PII data. Use Spring Security validation annotations to enforce strict input constraints.” “Here is a contextual code from a similar code module that passed all security checks and mitigates injection and XSS risks.”
“Encrypt PII when written to logs”	Ask context-aware questions such as: 1. Does this repository or application already use an approved logging and encryption framework? 2. If yes, which one (e.g., Log4j / Logback, SLF4J, Serilog, NLog, Python logging)? 3. Which version is approved, and what configuration pattern is standard in this environment? 4. Do we have approved controls for handling sensitive data in logs? (Masking or redaction vs. encryption, Tokenization, Structured logging policies) 5. What risks apply to this logging path? (e.g., PII leakage through debug or error logs) 6. What regulatory or policy requirements apply to this repository/application? (PCI DSS v4, GDPR / PII handling, Internal secure coding standards) Then, instead of static rule, generate a secure prompt, for example: “This repository/application is subject to GDPR and internal secure logging standards.” “This code module processes PII and writes logs using Logback (v1.4.2)” “Mask email addresses and phone numbers” “Hash customer identifiers using the approved hashing algorithm” “Strip authentication tokens and session IDs from all log levels”

Because in today’s world, software is never static – and neither should be the logic that protects it.

Top 3 Questions for Security Executives 

How do our security rules in the IDE adapt when a code change introduces new risk, for example when it:

1. Exposes PII (with a new format) in a new API?
2. Violates PCI DSS v4 requirement?
3. Contains a 0-day OSS vuln (like Shai-Hulud 2) in a high business impact code (e.g., authentication logic or payment processing service)?

If the answer to any of these questions is, “our rules don’t adapt at all,” then your developer-defined guidelines aren’t strengthening your security posture. They’re just creating the illusion of risk prevention.

Important to Remember

You can’t protect what you can’t see. → That applies to application security too.

Static rules worked yesterday.→ Dynamic software inventory and intelligence prevent tomorrow’s risk.