kandil_code 0.1.0

//! Ethics and security scanning agent
//! 
//! Specialized agent for performing security audits and ethics checks

use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use crate::core::agents::base::{Agent, AgentState};
use crate::core::adapters::ai::KandilAI;
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SecurityReport {
    pub vulnerabilities: Vec<Vulnerability>,
    pub risk_score: u8,  // 0-100 risk score
    pub compliance_status: ComplianceStatus,
    pub summary: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Vulnerability {
    pub id: String,
    pub title: String,
    pub description: String,
    pub severity: Severity,
    pub cve_id: Option<String>,
    pub owasp_category: Option<String>,
    pub recommendation: String,
    pub cvss_score: Option<f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum Severity {
    Info,
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplianceStatus {
    pub owasp_api_top_10: bool,
    pub sast_compliant: bool,
    pub data_protection_compliant: bool,
    pub privacy_compliant: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EthicsReport {
    pub bias_issues: Vec<BiasIssue>,
    pub privacy_concerns: Vec<PrivacyConcern>,
    pub ethical_risks: Vec<EthicalRisk>,
    pub recommendations: Vec<String>,
    pub ethics_score: u8,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BiasIssue {
    pub id: String,
    pub description: String,
    pub affected_area: String,
    pub severity: Severity,
    pub suggested_mitigation: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrivacyConcern {
    pub id: String,
    pub description: String,
    pub data_type: String,
    pub risk_level: Severity,
    pub recommendation: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EthicalRisk {
    pub id: String,
    pub description: String,
    pub impact_area: String,
    pub likelihood: String,
    pub suggested_controls: String,
}

pub struct EthicsSecurityAgent {
    ai: Arc<KandilAI>,
}

impl EthicsSecurityAgent {
    pub fn new(ai: Arc<KandilAI>) -> Self {
        Self { ai }
    }

    pub async fn security_scan(&self, code: &str, file_path: &str) -> Result<SecurityReport> {
        let prompt = format!(
            r#"Perform security audit of this code:
            File: {}
            
            Analyze for:
            - OWASP Top 10 vulnerabilities
            - Input validation issues
            - Authentication/authorization problems
            - Data exposure risks
            - Injection vulnerabilities
            - Security misconfigurations
            
            Code: {}
            "#,
            file_path, code
        );

        let result = self.ai.chat(&prompt).await?;
        
        // In a real implementation, this would parse the structured response
        // For now, we'll return a basic report
        Ok(SecurityReport {
            vulnerabilities: vec![
                Vulnerability {
                    id: "SEC-001".to_string(),
                    title: "SQL Injection".to_string(),
                    description: "Potential SQL injection vulnerability found".to_string(),
                    severity: Severity::High,
                    cve_id: Some("CVE-2023-0001".to_string()),
                    owasp_category: Some("A03:2021-Injection".to_string()),
                    recommendation: "Use parameterized queries".to_string(),
                    cvss_score: Some(8.5),
                }
            ],
            risk_score: 75,
            compliance_status: ComplianceStatus {
                owasp_api_top_10: false,
                sast_compliant: false,
                data_protection_compliant: true,
                privacy_compliant: true,
            },
            summary: "Security scan completed with critical issues identified".to_string(),
        })
    }

    pub async fn ethics_check(&self, code: &str, description: &str) -> Result<EthicsReport> {
        let prompt = format!(
            r#"Perform ethics audit of this system:
            Description: {}
            
            Analyze for:
            - Algorithmic bias
            - Privacy concerns
            - Fairness issues
            - Transparency problems
            - Data usage ethics
            - Potential societal harm
            
            Code: {}
            "#,
            description, code
        );

        let result = self.ai.chat(&prompt).await?;
        
        // For now, we'll return a basic report
        Ok(EthicsReport {
            bias_issues: vec![
                BiasIssue {
                    id: "ETH-001".to_string(),
                    description: "Potential bias in data processing".to_string(),
                    affected_area: "User recommendation algorithm".to_string(),
                    severity: Severity::Medium,
                    suggested_mitigation: "Add bias detection and mitigation measures".to_string(),
                }
            ],
            privacy_concerns: vec![
                PrivacyConcern {
                    id: "PRIV-001".to_string(),
                    description: "Collection of personal data without explicit consent".to_string(),
                    data_type: "Personal Information".to_string(),
                    risk_level: Severity::High,
                    recommendation: "Implement data minimization and consent management".to_string(),
                }
            ],
            ethical_risks: vec![
                EthicalRisk {
                    id: "ETH-002".to_string(),
                    description: "Potential for algorithmic discrimination".to_string(),
                    impact_area: "User treatment".to_string(),
                    likelihood: "Medium".to_string(),
                    suggested_controls: "Implement fairness checks and audit trails".to_string(),
                }
            ],
            recommendations: vec![
                "Add bias detection mechanisms".to_string(),
                "Implement privacy-by-design principles".to_string(),
            ],
            ethics_score: 65,
        })
    }
}

#[async_trait]
impl Agent for EthicsSecurityAgent {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "Given this security/ethics audit task: {}\n\nPlan the next audit step. What security or ethical aspect should we focus on?",
            state.task
        );
        
        self.ai.chat(&prompt).await
    }

    async fn act(&self, plan: &str) -> Result<String> {
        // Execute the planned security/ethics check
        let prompt = format!(
            "Perform this security/ethics audit: {}\n\nAnalyze for vulnerabilities and ethical concerns, providing specific findings.",
            plan
        );
        
        self.ai.chat(&prompt).await
    }

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze the security/ethics findings
        let prompt = format!(
            "Analyze these security/ethics audit results: {}\n\nWhat are the most critical risks that need to be addressed immediately?",
            result
        );
        
        self.ai.chat(&prompt).await
    }
}