In the post COVID-19 world, businesses are looking for ways to be more agile and reduce latency driven by traditional ways of working. This is not only reflected in their back-office processes but also in their customer facing ones. They are increasingly relying on hyper automation and artificial intelligence (AI) to solve complex business problems. Cybersecurity and risk programs should be part of this AI-led approach. With the need to reduce operating cost across enterprises, AI has become the hallmark for driving the 3Es – Efficiency, Economics and Experience.
Understanding what the AI technologies can do for you, how you can get past the adoption hurdles, and how to achieve non-linear growth in speed, scale and agility are some of the biggest challenges we face when considering the use of AI in cybersecurity and risk. In addition, use of AI solutions should not be interpreted as “replacing a team” but instead, complementing your team to increase productivity. As such, the use of AI should be core to your cybersecurity strategy.
Common AI methods applied across cyber
There are several techniques that are commonly used in AI such as deep learning neural networks, decision trees, Monte Carlo methods, clustering, ranking, linear classifiers (Fisher’s method, Support Vector Machine), Bayesian Statistical Inference, Markov chain, Linguistics, and Bias. These are broadly applied across seven methods, described below and in Figure 1:
- Machine Learning (ML) instills discipline, empowering systems with the ability to learn without explicitly being programmed. AI will find patterns within the data to predict the outcome of something that was never seen before, enabling cybersecurity and risk programs to scale without human intervention.
- Neural Network is a method by which a system develops and replicates how humans learn. It helps map relationships between enormous amounts of data and therefore gives the ability to translate data input of one form into a desired output, usually in another form.
- Robotics include design, build, and use of machines to perform tasks done traditionally by human beings using static/dynamic rules and machine models.
- Expert Systems emulate the decision-making ability of a human that has been designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than by conventional procedural code.
- Fuzzy Logic (FL) is a heuristic approach that resembles human reasoning. It allows for enhanced decision-tree processing and complements rules-based programming.
- Natural Language Processing (NLP) provides the ability to understand text in the same manner as a human being. Combining computational linguistics with statistical, machine learning, and deep learning models, these technologies enable computers to process human language in the form of text to understand its full meaning, so that they can automatically perform repetitive tasks.
- Named Entity Recognition (NER) extracts information from data. For using AI / ML models in the cyber security space, the contextualization needs to be very specific to understand the various terminologies of the security domain.