Ever dreamt of creating a cryptographic system that’s not just secure, but also learns and adapts? Welcome to the future! Today, we’re going to build an AI-powered cryptographic agent system that combines the robustness of classical encryption with the adaptability of machine learning. Our agents will perform hybrid encryption, generate digital signatures, detect anomalies, and intelligently recommend key rotations. Let’s dive in!
1. Setting the Stage
First, we import all the necessary libraries for cryptography, AI-based analysis, and data handling. We also define a `SecurityEvent` class to keep track of important events in our cryptographic system.
“`python
import hashlib, hmac, json, time, secrets, numpy as np
from dataclasses import dataclass
from typing import Dict, List
# … (other imports)
“`
2. Meet Our Crypto Agents
Next, we create the `CryptoAgent` class. Each agent has its own set of keys, session storage, and security tracking system. They can generate and exchange RSA public keys and establish secure hybrid-encrypted sessions.
“`python
class CryptoAgent:
def __init__(self, agent_id: str):
# … (initialization)
pass
def get_public_key_bytes(self) -> bytes:
# … (get public key)
def establish_session(self, partner_id: str, partner_public_key_bytes: bytes) -> bytes:
# … (establish session)
def receive_session_key(self, partner_id: str, encrypted_session_key: bytes):
# … (receive session key)
“`
3. Secure Communication
Our agents can now communicate securely using AES-GCM. Each message is encrypted, signed, risk-scored, and then safely decrypted by the recipient, ensuring authenticity and confidentiality.
“`python
def encrypt_message(self, partner_id: str, plaintext: str) -> Dict:
# … (encrypt message)
def decrypt_message(self, encrypted_msg: Dict) -> str:
# … (decrypt message)
“`
4. AI-Driven Security
We add advanced AI-driven components that analyze encryption behavior, detect anomalies, and log risk events. Our agents now have adaptive intelligence, can identify unusual patterns, and generate a complete security report.
“`python
def analyze_encryption_pattern(self, message_length: int) -> float:
# … (analyze encryption pattern)
def log_security_event(self, event_type: str, risk_score: float, details: Dict):
# … (log security event)
def generate_security_report(self) -> Dict:
# … (generate security report)
“`
5. The Grand Demo
Finally, we demonstrate the full cryptographic workflow. Two agents, Alice and Bob, securely exchange messages, detect anomalies, and review detailed security reports.
“`python
def demo_crypto_agent_system():
# … (demo)
“`
In conclusion, we’ve shown how artificial intelligence can enhance traditional cryptography by introducing adaptability and context awareness. Our agents don’t just encrypt and authenticate messages; they learn from communication behavior and dynamically adjust security measures. Combining AI-driven analytics with hybrid encryption empowers a new generation of intelligent, self-monitoring cryptographic systems.
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