https://github.com/security-decision-science/decision-security

Reusable decision-science utilities for security: Monte Carlo, Bayes, Survival, VoI, light causal helpers.
https://github.com/security-decision-science/decision-security

bayesian causal-inference cybersecurity decision-science monte-carlo risk-quantification survival-analysis value-of-information

Last synced: 6 months ago
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Reusable decision-science utilities for security: Monte Carlo, Bayes, Survival, VoI, light causal helpers.

• Host: GitHub
• URL: https://github.com/security-decision-science/decision-security
• Owner: security-decision-science
• License: mit
• Created: 2025-10-03T14:19:16.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-10-16T20:39:07.000Z (9 months ago)
• Last Synced: 2026-01-08T21:39:10.569Z (6 months ago)
• Topics: bayesian, causal-inference, cybersecurity, decision-science, monte-carlo, risk-quantification, survival-analysis, value-of-information
• Language: Python
• Homepage:
• Size: 154 KB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Decision Security

Reusable **decision-science utilities for security** — Monte Carlo risk bands, Bayesian updates & calibration, survival helpers, Value of Information, light causal helpers, and visualization.

## Install

Pre-release for now:

```bash

pip install --pre decision-security

# or pin:

# pip install decision-security==0.1.0a9 

```

## Quickstart

```python

import numpy as np

from decision_security.montecarlo import risk_bands, var_es, make_lognormal_severity, simulate_aggregate_losses

sev = make_lognormal_severity(meanlog=8.0, sdlog=1.2)

losses = simulate_aggregate_losses(n_periods=10000, lam=0.6, severity_sampler=sev)

print(risk_bands(losses))      # {'p50': ..., 'p90': ..., 'p95': ...}

print(var_es(losses))          # (VaR95, ES95)

```

## Modules

	•	synth: synthetic data (heavy-tail losses, counts, mixtures, survival with censoring, categorical/Dirichlet).

	•	montecarlo: Poisson frequency + severity, risk bands, VaR/ES.

	•	bayes: Beta-Binomial & Normal(known σ) updates, calibration helpers.

	•	survival: simple Kaplan–Meier & Nelson–Aalen estimates.

	•	voi: Expected Value of Perfect Information (EVPI) and simple ROI selection.

	•	causal: tiny DAG utilities (parents, descendants, naive backdoor set).

	•	viz: small matplotlib helpers (loss distribution, risk bands, KM curves).

## Status

0.x (APIs may change).

## Docs & examples 

Security Decision Science Book and the Security Decision Labs playground (coming soon).

## Contributing

Issues and PRs welcome. For non-public questions, contact me on LinkedIn.