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Zero false positives

Expert intelligence + effective automation

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Risk indicator roundup

A matter of taste

What is the best risk indicator? Bottom line: there is no "best", only different approaches to the same thing. Ultimately, it’s up to you. Here we will show the pros and cons of each so you can make an informed decision (about that which will guide your informed decisions …

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Seek for chaos and dive into it

The Antifragile philosophy

Imagine a medium-sized sealed carton box, with two or three glasses inside. If you kick the box (like kicking a soccer ball), the glasses will surely break. The glasses are fragile. Now, think of the same box, but with two or three standard steel hammers. Nothing will happen to those …

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Great Expectations

What to expect when you're at risk

Thus far, the situations we have modeled have been either over-simplifications or fabrications in order to illustrate a concept. This article will try to improve on that a bit by considering more variables and closer to reality, too. We will do so by presenting the subject matter needed to understand …

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The F*CK strategy

The pratfall effect application on business

Do you like fried chicken? A year ago or so, KFC -the chicken fast-food chain- was featured in almost every news outlet in the UK: they ran out of chicken for an entire weekend. A horror story for a food-chain with 900 restaurants in the country. People were mad at …

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Quantitative Python

Risk management with Python

Now that we have an understanding of risk concepts such as the loss exceedance curve, value-at-risk, Bayes Rule, and fitting distributions, we would like to have a realiable, extensible and preferably open tool to perform these computations. In the background, we have used a spreadsheet, which is hard to extend …

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Para bellum

Prepare for the worst risk

"Si vis pacem, para bellum", goes the old adage. If you want peace, prepare for war. In our case, the worst possible risky scenario our information assets could go into. While probability distributions, loss exceedance curves, simulated scenarios, etc, are all great for the quants in the office, at the …

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Hit or miss

Estimating attack probability

One of the main obstacles against adopting a quantitative approach to risk management is that since major security breaches are relatively rare and hence, there cannot be enough data for proper statistical analysis. While this might be true in the classical sense, it is not if we adopt a Bayesian …

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Updating your beliefs

How Bayes Rule affects risk

Usually, changing our beliefs is seen as a negative thing. But when those beliefs represent our state of uncertainty regarding a particular cybersecurity risk, you’d better use all the tools at hand to reduce that uncertainty, i.e., measuring. Why do we speak of "belief" and not "probability" here …

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Monetizing vulnerabilities

From probabilites to dollars and cents

In our previous article, we merely scratched the surface of the problem that quantifying risks poses, barely touching on concepts such as calibrated estimation, confidence intervals and specifying the measuring object. Now that (if?) we are convinced that: Cybersecurity risk can and should be measured in...

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Quantifying risk

From color scales to probabilities and ranges

One of the least understood parts of a vulnerability is the risk it poses to the target. On the client side, it tends to get confused with impact and occurrence likelihood, due to devices like the so-called “risk matrix”, which are supposed to help us better understand risks: Figure 1 …

Fluid Attacks, Among the Top Global Leaders 2018

Among the Top Global Leaders 2018

Fluid Attacks, a top cybersecurity company

In an era where IT companies and business services are diverse, realizing which companies have the best performance on service delivery is crucial when buying services from companies that guarantee their expertise. Having this in mind, Clutch (a renowned platform that connects buyers with IT companies) has...

Person playing chess against a robotic arm

Will machines replace us?

Automatic detection vs. manual detection

More than 20 years have passed since Garry Kasparov, the chess world champion, was defeated by Deep Blue, the supercomputer designed by IBM. For many people, that event was proof that machines had managed to exceed human intelligence [1]. This belief raised many doubts and concerns regarding technological advance, that …

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