openfair

Crates.ioopenfair
lib.rsopenfair
version0.1.1
sourcesrc
created_at2021-07-28 10:24:05.167679
updated_at2021-07-28 10:24:05.167679
descriptionFactor Analysis of Information Risk (OpenFAIR)
homepage
repositoryhttps://github.com/marirs/openfair-rs
max_upload_size
id428295
size85,005
SG (marirs)

documentation

README

OpenFAIR-RS

Factor Analysis of Information Risk (OpenFAIR) is a model/method to help organizations understand the level of risk present in their IT environments.

The FAIR methodology is conceived as a way to provide meaningful measurements so that it could satisfy management's desire to make effective comparisons and well-informed decisions. FAIR has become the only international standard Value at Risk (VaR) model for cyber-security and operational risk.

FAIR is a methodology for analyzing cybersecurity risk. Here, we will refer to risk as the total dollar amount of expected loss for a given timeframe. In a general sense, FAIR methodology works by breaking risk into its individual components. These components can then be measured or estimated numerically, allowing for a quantitative calculation of risk as a whole.

The actual calculation for risk often takes the form of a Monte Carlo method.

This is based on the terms found in:

  1. Open FAIRâ„¢ RISK TAXONOMY (O-RT)
  2. Open FAIRâ„¢ RISK ANALYSIS (O-RA)

"Open FAIR" is a trademark of the Open Group.

Requirements

  • Rust 1.50+

Usage

  • Cargo.toml
[dependencies]
openfair = "0.1.1"

and then

use openfair::{simulate, Result};
use std::fs::read_to_string;

fn main() -> Result<()> {
    let input = read_to_string("data/input.json")?;
    let result = simulate(&serde_json::from_str(&input)?)?;
    println!("{:#?}", result);
    
    Ok(())
}

Example

  • Running the example to return the output
cargo run --example eg1 -- -i data/input.json
  • Running the example to return the chart data output
cargo run --example eg1 -- -i data/input.json --generate-chart

Reference


License: MIT/Apache 2.0

Commit count: 0

cargo fmt