Back to the blog

AI IN SOFTWARE SECURITY

author image

Binoy Suvarnadas

Technology | January 22, 2020

hero image

Software testing and test automation are changing in the AI era. With the adoption of digital transformation across domains, security and engineering teams are constantly identifying ways to keep up with continuous integration and maintain shorter release cycles. 100 percent coverage in software test automation is every software engineering team’s dream. Can we achieve that in the next decade or so? Yes, it seems like artificial intelligence will not only help us to automate the full scope but will also help to maintain test scripts automatically, predict futuristic threats and even suggest solutions to fix issues.

Inefficient testing practices have created challenges in managing the test cycles. Organizations are trying at scale to avoid waste cycles in manual and automation procedures. Current automation solutions play an anticipatory role and have limited prediction capabilities. Bringing in a self-learning solution is the futuristic and modern approach to reduce waste cycles. With continuous regression cycles taking the toll on test and security engineers, AI and ML-based solutions are helping software test teams to adapt and respond to challenges in the DevOps environment.

The value of artificial intelligence solutions comes from minimizing the direct involvement of the teams to complete the most tedious and repetitive jobs. Learning solutions can identify the status of each test, most recent code changes and decide on which test to run. Artificial agents or bots are now capable to test applications at a scale that enables faster delivery ensuring higher quality and secure end-user experiences. The current operational ineffectiveness in decision making accuracies can be overwritten by these artificial bots, ultimately reducing the complexity of analysis performed by humans. These platforms can now create smart scenarios, run autotests, clone instances as and when required, identify variations and maintain the test themselves. This helps testing teams to achieve higher test coverage through efficient running cycles within short time frames.

As every industry is innovating and adapting to custom artificial intelligence-based solutions, there are multiple challenges like identifying use cases, identifying the behavior of the apps and testing against performance, scalability, and security. Teams that have incorporated machine learning-based test solutions have achieved expected coverage thereby generating higher output. Quality. Integrating and automating key use cases have helped to analyze complex data sets effectively, reveal actionable insights and track responses. With deep learning capabilities these systems can quickly prototype use cases and develop patterns that can be used to create various models.

AI security platforms running continuously at target ends are capable of profiling millions of files, logs, and samples in a matter of hours and assist security teams to learn continuously and be proactive in vulnerability analysis and exploitation. By processing different subsets of data these systems can help in generating metrics, structure out network infrastructure and log results based on instructions through learning and mining. In addition, AI scans can generate a high volume of data from which actionable insights can be generated more efficiently. This reduces operational costs as well as the time to process and see the reports.

Modern AI platforms come with a plug and play filters, contextual warnings, less false positives and work across platforms. With advanced features, the platforms can integrate with teams to automatically secure all the digital interactions that happen across enterprises and their clients. Multilayered approaches are used to detect data transactions without compromising end user’s experience. Systems can now monitor and analyze live transactions of each user and recognize unusual patterns by precisely monitoring each endpoint. Identifying deviations from normal transactions is the key to identify the intrusions.

Automated code attacks can happen anytime, and an intrusion can be devastating in a matter of seconds. While traditional devices check catalogs, AI-based platforms create catalogs that can emerge in the future by learning and classifying a large set of data in limited time. ML algorithms run through connected devices, usage data, contents and privacy risks from a vast amount of network data. These scans provide actionable insights that human brains cannot handle in a certain time frame. In software and cybersecurity, this enables teams to identify threats and spots as they arise or predict when things can go wrong.

AI integrations are most useful in some of the areas like System Security, Threat Intelligence, Threat Detection, Threat Hunting, and Vulnerability Management. Some of the key advantages in AI-enabled security testing are
 More accurate results
 Compare and learn in less time
 Scalability
 Limit dependence on human skills
 More outputs, more execution cycles
 Prediction capabilities

Today’s AI-based systems combine human intelligence, data science, and machine learning. Advanced features like automating threat investigations can save a lot of time and open innovative test model design techniques. As technology, machines, and data scale each minute, the reputation of every enterprise in aviation, manufacturing, financial services, maritime, oil and gas, utilities, defense, and automobile sector are under constant threat to cybersecurity attacks. Collaboration between intelligence systems and human expertise is still required to develop frontline security solutions for these sectors. As enterprises struggle to keep up with on growing demands, artificial intelligence is all set to solve the problems by proactively discovering loopholes and helping teams to resolve issues at a swift pace. With agile AI solutions integrated via the cloud, teams can streamline operations of any scale and accomplish set objectives.

Browse all categories