Can AI be Used for Security Testing and Vulnerability Detection?
AI can assist cybersecurity teams in quickly detecting and responding to threats in real time, helping reduce dwell times of attackers on networks while helping reduce costs related to financial losses, reputational damage and regulatory penalties.
Cybercriminals may attack AI applications by interfering with how decisions are reached or by discovering which data was used in training them – this type of attack is known as an “inference attack.”
Artificial Neural Networks are algorithms inspired by the structure and function of human neural networks. As such, they have become a widely-used tool in data science for applications like image recognition, speech recognition, natural language processing, natural language understanding (NLP), intrusion detection (IDS) etc.
An artificial neural network consists of a layer of nodes linked by connections. Each node in the network has inputs, outputs and weights of its own; inputs multiply with weights to produce results which are passed to an activation function which determines what it represents; it then selects which neurons in the hidden layer should fire according to this representation – those which fire will move to the output layer only if they do so first.
Machine learning makes it much simpler for humans to identify the content in images, identifying it from past examples and helping protect your data by providing an extra layer of security against hackers attempting to find ways of breaching networks while sidestepping traditional security measures.
One drawback of neural networks is their opaque nature; once they begin producing results it can be hard to know exactly how they got there. However, researchers working on explaining AI (Explainable AI) offer ways for people to gain more understanding into how neural network algorithms function and why they behave the way they do.
Machine learning is an algorithmic approach to parsing data and making decisions from it. Rather than being limited by predetermined rules or directives from humans, machine learning learns what normal behavior looks like over time through repeated experiences in different environments and can even identify potentially dangerous patterns.
Vulnerabilities in software represent one of the main threats to cybersecurity, leading to data breaches. Without AI-powered methods for detection vulnerabilities such as static analysis or reinforcement learning and deep learning approaches can often take too long and be too inaccurate in their results. AI can improve these efficiencies.
VAPT (vulnerability assessment and penetration testing) processes can be extremely time-consuming due to the sheer volume of vulnerabilities to be tested for. AI-powered security solutions can use GPU computing power on laptops to speed up this process and take remediation actions as soon as they become aware.
This can help address the resource crunch facing cybersecurity, freeing up valuable people to focus on more complex issues. Furthermore, DevOps-style frameworks enable organizations to scale automated, accurate and actionable security testing solutions quickly.
The challenge in protecting an AI model against attacks that exploit its underlying algorithms and workflows to manipulate its behavior in ways unintended by its designer, such as changing training algorithms or altering behavior so as to cause malicious responses from it. Such attacks have already been observed in several instances and require extra precaution to counter.
Deep Learning is a subfield of machine learning which utilizes several layers of algorithms to perform complex computations. It has found use in areas like computer vision and speech recognition; more recently it has also been applied to source code analysis to detect software security vulnerabilities; however results have been mixed; some approaches such as simple bag-of-words classification algorithms failed to capture source code’s sequential nature while graph analysis and neural networks performed better, reaching precision/recall accuracy levels of over 80% in some instances.
AI/ML can also be applied to red teaming and penetration testing (PST), an integral feature of many cybersecurity platforms as a way of testing whether their system can withstand cyber attacks. They often incorporate the MITRE ATT&CK framework, an open-source collection of adversary techniques and attack patterns designed to help teams assess their systems’ defenses against attack.
At the reconnaissance stage of a penetration test, machine learning (ML) can be used to automatically gather publicly available information about a system under test, greatly decreasing time and effort spent manually gathering this data, increasing chances of successfully infiltrating it. Furthermore, automated vulnerability scanning of systems may reveal risks missed by human testers that have yet to be noticed by ML systems.
Natural Language Processing
Natural Language Processing (NLP) is the practice of using computers to interpret human speech. NLP can be used for translation, text summarization and document generation – among many other functions. NLP is one of AI’s fastest-growing areas with applications across industries from social media posts to call transcripts – even text-to-speech apps and smart speakers!
NLP can also help enhance security by helping machines understand the context of text and data, detect anomalies or suspicious patterns in data, prevent errors by automatically correcting inputs, as well as identify risks or threats missed through traditional means.
Unfortunately, natural language processing (NLP) is vulnerable to attacks from adversaries. A malicious actor could attempt to disorient an NLP model by switching adjacent characters or scrambling them – potentially through ITO artifacts or legal documents – so as to misinterpret its results and create confusion for its model.
NLP presents several other important challenges. First, NLP models must be trained for high accuracy; otherwise they could become inaccurate and become an obstacle in security testing and vulnerability detection. Furthermore, if not debiased adequately, NLP may lead to biases; this can become problematic in sectors like banking where regulators must ensure the system doesn’t discriminate against certain groups.
For more, check out our post of the current privacy concerns on TikTok