What are Single Loss Expectancy, Annualized Rate of Occurrence and Annualized Loss Expectancy?

by | Feb 23, 2021 | CompTIA, Exclusive Articles, Featured, Governance, Risk and Compliance, Information Security

Single Loss Expectancy (SLE), Annual Rate of Occurrence (ARO) and Annualized Loss Expectancy (ALE)

Let’s say there is a risk on an asset. If the risk occurs on the asset, then there is going to be a monetary loss. Single Loss Expectancy (SLE) is the monetary loss expected from an occurrence of a risk on an asset.

Single Loss Expectancy (SLE) is expressed as:

Single Loss Expectancy (SLE) = Asset Value x Exposure Factor

Asset value is the value of the asset. And, exposure factor is the impact of the risk on the asset.

This article is accessible to premium members only. To access this article, please purchase The Security Buddy Premium Membership Plan.

Pages: 1 2
Facebooktwitterredditpinterestlinkedinmail

Related posts:

Risk RegisterWhat is a risk register? Risk Heat MapWhat is a risk heat map? Business Risk Management StrategiesWhat are the different risk management strategies? DLPWhat is Data Loss Prevention (DLP)? Authentication FactorWhat are authentication factors? Risk Management ProcessRisk Management Process Risk Appetite vs. Risk ToleranceWhat are risk appetite and risk tolerance? 2FAWhat is 2 Factor Authentication? Inherent Risks, Residual Risks And RiskControlsWhat are inherent risks, residual risks and risk controls? LogLossCalculate The Logistic Loss Score using sklearn in Python Business Impact Analysis (BIA)What is the Business Impact Analysis (BIA)? Cross-Entropy LossWhat is cross-entropy loss? Different Types of Business RisksWhat are the different types of business risks? Security ControlsWhat are security controls? Identity Based EncryptionWhat is Identity-Based Encryption (IBE)? HOTP and TOTPWhat are HOTP and TOTP and how do they work?

Calculate the pseudoinverse of a matrix using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

What is the pseudoinverse of a matrix? We know that if A is a square matrix with full rank, then A-1 is said to be the inverse of A if the following condition holds: $latex AA^{-1}=A^{-1}A=I $ The pseudoinverse or the Moore-Penrose inverse of a matrix is a...
Read More

Cholesky decomposition using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

What is Cholesky decomposition? A square matrix A is said to have Cholesky decomposition if it can be written as a product of a lower triangular matrix and its conjugate transpose. $latex A=LL^{*} $ If all the entries of A are real numbers, then the conjugate...
Read More

Tensor Hadamard Product using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

In one of our previous articles, we already discussed what the Hadamard product in linear algebra is. We discussed that if A and B are two matrices of size mxn, then the Hadamard product of A and B is another mxn matrix C such that: $latex H_{i,j}=A_{i,j} \times...
Read More

Perform tensor addition and subtraction using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

We can use numpy nd-array to create a tensor in Python. We can use the following Python code to perform tensor addition and subtraction. import numpy A = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) B = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) C =...
Read More

How to create a tensor using Python?

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

What is a tensor? A tensor is a generalization of vectors and matrices. It is easily understood as a multidimensional array. For example, in machine learning, we can organize data in an m-way array and refer it as a data tensor. Data related to images, sounds, movies,...
Read More

How to combine NumPy arrays using horizontal stack?

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

We can use the hstack() function from the numpy module to combine two or more NumPy arrays horizontally. For example, we can use the following Python code to combine three NumPy arrays horizontally. import numpy A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) B =...
Read More

How to combine NumPy arrays using vertical stack?

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

Let’s say we have two or more NumPy arrays. We can combine these NumPy arrays vertically using the vstack() function from the numpy module. For example, we can use the following Python code to combine three NumPy arrays vertically. import numpy A = numpy.array([[1, 2,...
Read More

Singular Value Decomposition (SVD) using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

What is Singular Value Decomposition (SVD)? Let A be an mxn rectangular matrix. Using Singular Value Decomposition (SVD), we can decompose the matrix A in the following way: $latex A_{m \times n}=U_{m \times m}S_{m \times n}V_{n \times n}^T $ Here, U is an mxm matrix....
Read More

Eigen decomposition of a square matrix using Python

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

Let A be a square matrix. Let’s say A has k eigenvalues λ1, λ2, ... λk. And the corresponding eigenvectors are X1, X2, ... Xk. $latex X_1=\begin{bmatrix} x_{11} \\ x_{21} \\ x_{31} \\ ... \\ x_{k1} \end{bmatrix} \\ X_2=\begin{bmatrix} x_{12} \\ x_{22} \\ x_{32} \\ ......
Read More

How to calculate eigenvalues and eigenvectors using Python?

by | October 3, 2023 | Featured, Linear Algebra | 0 Comments

In our previous article, we discussed what eigen values and eigenvectors of a square matrix are and how we can calculate the eigenvalues and eigenvectors of a square matrix mathematically. We discussed that if A is a square matrix, then $latex (A- \lambda I) \vec{u}=0...
Read More

Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

0 Comments

Submit a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Not a premium member yet?

Please follow the link below to buy The Security Buddy Premium Membership.

Buy Premium Membership

Featured Posts

Translate »