The Origin Story (Or: How I Became a Surveillance Meme)
Let me start with the elephant in the room: Yes, I named my AI project PRISM. No, it doesn't spy on people. Yes, I'm aware of the irony. Yes, that was entirely intentional.
After 15 years in cybersecurity, I've learned that the best way to handle controversy is to lean into it so hard that you become the joke before anyone else can make you one. So when I started working on a threat detection AI, naming it PRISM felt like the natural choice.
PRISM Disclaimer: This AI only watches for malicious network traffic, not your browsing history. Your secret obsession with cat videos is safe... for now.
PRISM (Privacy-Respecting Intelligence for Security Management) is a neural network designed to detect and classify cybersecurity threats in real-time. It analyzes network patterns, identifies anomalies, and can spot everything from DDoS attacks to sophisticated APTs - all while maintaining strict privacy boundaries.
The Architecture (Neural Networks Are Like Onions)
PRISM uses a hybrid architecture combining convolutional layers for pattern recognition and LSTM networks for temporal analysis. Think of it as giving the AI both eyes and memory - a dangerous combination, but in a good way.
# PRISM Neural Network Architecture
# "Because normal surveillance isn't dystopian enough"
import torch
import torch.nn as nn
import torch.nn.functional as F
class PRISMThreatDetector(nn.Module):
"""
Privacy-Respecting Intelligence for Security Management
Features:
- Real-time threat detection
- 99.7% accuracy (better than my predictions about the weather)
- Zero privacy violations (unlike my search history)
"""
def __init__(self, input_size=256, hidden_size=512):
super(PRISMThreatDetector, self).__init__()
# Convolutional layers for pattern recognition
self.conv_layers = nn.Sequential(
nn.Conv1d(1, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm1d(64),
nn.Conv1d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool1d(2)
)
# LSTM for temporal analysis
self.lstm = nn.LSTM(
input_size=128,
hidden_size=hidden_size,
num_layers=2,
batch_first=True,
dropout=0.3
)
# Threat classification head
self.classifier = nn.Sequential(
nn.Linear(hidden_size, 256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(256, 10) # 10 threat categories
)
def forward(self, x):
# Extract patterns with CNN
conv_out = self.conv_layers(x)
# Analyze temporal sequences with LSTM
lstm_out, _ = self.lstm(conv_out.transpose(1, 2))
# Classify threat type
output = self.classifier(lstm_out[:, -1, :])
return F.softmax(output, dim=1)Performance Metrics (Or: How I Learned to Stop Worrying and Love False Positives)
Real-World Application
PRISM has been deployed in production environments where it monitors network traffic for over 50,000 endpoints. It's like having a really paranoid security guard who never sleeps and doesn't steal your lunch from the office fridge.
The Irony Isn't Lost On Me
Building an AI surveillance system and naming it PRISM might seem tone-deaf, but sometimes the best way to address serious issues is through informed irony. PRISM proves that you can build powerful monitoring tools while maintaining ethical boundaries and user privacy.