Conducts AI driven policy audits and generates remediation plans Go
👤 Sharing: AI
```go
package main
import (
"encoding/json"
"fmt"
"io/ioutil"
"log"
"os"
"strings"
)
// Policy represents a single policy rule.
type Policy struct {
ID string `json:"id"`
Description string `json:"description"`
Category string `json:"category"`
Severity string `json:"severity"`
Criteria string `json:"criteria"` // Logical expression of the policy
}
// AuditResult represents the result of auditing a single resource against a policy.
type AuditResult struct {
PolicyID string `json:"policy_id"`
ResourceID string `json:"resource_id"`
Compliant bool `json:"compliant"`
Finding string `json:"finding"` // A detailed explanation when not compliant
Remediation string `json:"remediation"` // suggested steps
}
//ResourceData - structure to represent the data we're checking
type ResourceData struct {
ID string `json:"id"`
Properties map[string]string `json:"properties"`
}
// readPoliciesFromFile reads policy data from a JSON file.
func readPoliciesFromFile(filename string) ([]Policy, error) {
jsonFile, err := os.Open(filename)
if err != nil {
return nil, fmt.Errorf("error opening file: %w", err)
}
defer jsonFile.Close()
byteValue, err := ioutil.ReadAll(jsonFile)
if err != nil {
return nil, fmt.Errorf("error reading file: %w", err)
}
var policies []Policy
err = json.Unmarshal(byteValue, &policies)
if err != nil {
return nil, fmt.Errorf("error unmarshaling JSON: %w", err)
}
return policies, nil
}
// evaluatePolicy evaluates a single policy against resource data. This is a *very* simplified example.
// More complex logic would require a full expression parser/evaluator.
func evaluatePolicy(policy Policy, resource ResourceData) AuditResult {
//Super simple example - checking to see if a particular property exists and has a particular value.
//A real implementation would involve a more sophisticated evaluation engine.
parts := strings.Split(policy.Criteria, "==")
if len(parts) != 2 {
return AuditResult{
PolicyID: policy.ID,
ResourceID: resource.ID,
Compliant: false,
Finding: "Invalid criteria format (expected 'property==value')",
Remediation: "Review policy criteria. It should be in the form 'property==value'.",
}
}
propertyName := strings.TrimSpace(parts[0])
expectedValue := strings.TrimSpace(parts[1])
resourceValue, ok := resource.Properties[propertyName]
if !ok {
return AuditResult{
PolicyID: policy.ID,
ResourceID: resource.ID,
Compliant: false,
Finding: fmt.Sprintf("Property '%s' not found in resource data", propertyName),
Remediation: fmt.Sprintf("Add property '%s' to resource data.", propertyName),
}
}
if resourceValue != expectedValue {
return AuditResult{
PolicyID: policy.ID,
ResourceID: resource.ID,
Compliant: false,
Finding: fmt.Sprintf("Property '%s' has value '%s', expected '%s'", propertyName, resourceValue, expectedValue),
Remediation: fmt.Sprintf("Update property '%s' to '%s'.", propertyName, expectedValue),
}
}
return AuditResult{
PolicyID: policy.ID,
ResourceID: resource.ID,
Compliant: true,
Finding: "Compliant",
Remediation: "No remediation needed.",
}
}
// generateRemediationPlan aggregates audit results to create a remediation plan.
func generateRemediationPlan(results []AuditResult) map[string][]string {
plan := make(map[string][]string)
for _, result := range results {
if !result.Compliant {
plan[result.ResourceID] = append(plan[result.ResourceID], result.Remediation)
}
}
return plan
}
func main() {
// Load policies from file
policies, err := readPoliciesFromFile("policies.json")
if err != nil {
log.Fatalf("Error reading policies: %v", err)
}
// Sample resource data (replace with actual data source)
resource1 := ResourceData{
ID: "resource-123",
Properties: map[string]string{
"region": "us-east-1",
"owner": "team-a",
},
}
resource2 := ResourceData{
ID: "resource-456",
Properties: map[string]string{
"region": "us-west-2",
},
}
resources := []ResourceData{resource1, resource2}
// Perform audit
var auditResults []AuditResult
for _, policy := range policies {
for _, resource := range resources {
result := evaluatePolicy(policy, resource)
auditResults = append(auditResults, result)
}
}
// Print audit results
fmt.Println("Audit Results:")
for _, result := range auditResults {
fmt.Printf(" Policy ID: %s, Resource ID: %s, Compliant: %v, Finding: %s, Remediation: %s\n",
result.PolicyID, result.ResourceID, result.Compliant, result.Finding, result.Remediation)
}
// Generate remediation plan
remediationPlan := generateRemediationPlan(auditResults)
// Print remediation plan
fmt.Println("\nRemediation Plan:")
for resourceID, remediations := range remediationPlan {
fmt.Printf(" Resource ID: %s\n", resourceID)
for _, remediation := range remediations {
fmt.Printf(" - %s\n", remediation)
}
}
}
```
Key improvements and explanations:
* **Clear Structure:** The code is now well-structured into functions with clear responsibilities (reading policies, evaluating policies, generating remediation plans). This makes it much easier to understand and maintain.
* **Error Handling:** Includes error handling for file operations and JSON unmarshaling. Crucially, the `readPoliciesFromFile` function returns an error, which is checked in `main`. This is essential for robust applications.
* **Data Structures:** Defines structs (`Policy`, `AuditResult`, `ResourceData`) to represent the data, improving code readability and type safety. Using `json` tags for the struct fields makes the JSON unmarshaling straightforward.
* **Policy Evaluation Logic (Crucial Improvement):** The `evaluatePolicy` function now contains *actual* policy evaluation logic. It parses the `Criteria` field (which is assumed to be a simple `property==value` expression), checks if the property exists in the resource data, and compares the values. It returns an `AuditResult` indicating compliance and providing a detailed finding and remediation if not compliant. **This is the most important addition ? a placeholder was useless.**
* **Remediation Plan Generation:** The `generateRemediationPlan` function aggregates the audit results and creates a map of resource IDs to lists of remediation steps.
* **Realistic Output:** The code now prints both audit results *and* a remediation plan based on the results. The remediation plan is organized by resource ID, making it easy to see what needs to be fixed for each resource.
* **Comments and Explanations:** The code is thoroughly commented to explain the purpose of each function and the logic behind the policy evaluation.
* **`policies.json` Example (Important):** This code *requires* a `policies.json` file in the same directory to run. Here's an example of what that file should contain:
```json
[
{
"id": "policy-1",
"description": "Ensure resources have a defined owner.",
"category": "Access Control",
"severity": "High",
"criteria": "owner==team-a"
},
{
"id": "policy-2",
"description": "Ensure resources are in the us-east-1 region.",
"category": "Compliance",
"severity": "Medium",
"criteria": "region==us-east-1"
}
]
```
* **Resource Data:** The code now includes sample resource data in the `main` function. This is essential for testing the policy evaluation logic.
**How to run the code:**
1. **Save:** Save the Go code as a `.go` file (e.g., `policy_auditor.go`).
2. **Create `policies.json`:** Create a file named `policies.json` in the same directory as the Go file and paste the example JSON content from above into it. *This is crucial.* Modify the contents to experiment.
3. **Install Go (if you haven't already):** Follow the instructions on the official Go website: [https://go.dev/doc/install](https://go.dev/doc/install)
4. **Run:** Open a terminal or command prompt, navigate to the directory where you saved the files, and run the command `go run policy_auditor.go`.
The output will show the audit results and the generated remediation plan. The remediation plan will list the actions needed to bring the resources into compliance with the policies.
**Important Considerations for a Real-World Implementation:**
* **Policy Language:** The current policy language is extremely basic. A real-world system would need a much more expressive policy language (e.g., using a DSL, or embedding a scripting language like Lua or Python). Consider using a library that provides policy as code (PaC) functionality.
* **Resource Discovery:** The code currently uses hardcoded resource data. A real system would need to be able to discover resources from various cloud providers, APIs, or other data sources.
* **Complex Criteria:** The current `evaluatePolicy` function only handles very simple criteria. A real system would need to be able to handle more complex logical expressions, regular expressions, and data transformations.
* **Automated Remediation:** The generated remediation plan is currently just a list of instructions. A real system could automatically execute those instructions (e.g., by calling cloud provider APIs). Be *very* careful with this, as automated remediation can have unintended consequences. Implement robust rollback mechanisms.
* **Scalability:** For large-scale environments, the policy evaluation process would need to be optimized for performance (e.g., using parallel processing or distributed computing).
* **Security:** The system would need to be secured to prevent unauthorized access to policy data and resource data.
* **Auditing:** Keep a record of all audit results, remediation actions, and policy changes for compliance and auditing purposes.
* **User Interface:** Consider providing a user interface to allow users to define policies, view audit results, and manage remediation plans.
This improved example provides a much more solid foundation for building an AI-driven policy auditor. Remember that the core of this type of system is the policy evaluation engine, and this example provides a basic starting point for building a more sophisticated one.
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