Agile Security Operations by Hinne Hettema

Author:Hinne Hettema
Language: eng
Format: epub
Publisher: Packt Publishing Pvt Ltd
Published: 2021-12-15T00:00:00+00:00

After going through data controls as a root of trust, let's look at algorithmic integrity as a root of trust.

Algorithmic integrity as a root of trust

Algorithms are what transform data into other data. Increasingly, algorithmic integrity is becoming important as a factor in how we make decisions with big data and AI attacks. AI attacks are a new category of cyberattacks that focuses on subverting the AI algorithm itself.

Algorithmic integrity focuses on whether we can trust our algorithm to work as planned. For most algorithms, this can be verified as a matter of code integrity and functionality testing under varying scenarios. With AI attacks, risks to algorithmic integrity consist of the following:

Bias: Bias can be introduced when and where an attacker influences or modifies the dataset that an algorithm is being trained on. Bias often occurs in AI on its own, when the creators of models do not take a sufficient variety of inputs into account.

Input modification: AI algorithms, at a very abstract level, are black boxes that take inputs (data) and produce outputs (verdicts, decisions, and actions). Because of their black-box nature, it can be hard to relate inputs to outputs, and an attacker who can take control of an input stream can influence the output.

Model poisoning: Model poisoning focuses on the learning stage of AI and aims to subvert the learning of the model to make it produce an output desired by the attacker.

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