Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are inadequate and unreceptive to changes in the system, causing longer response times and organization needs exposure to emerging attack vectors. In light of the previous constraints, we introduce AutoGuard to the DevSecOps ecosystem, a reinforcement learning (RL)-powered self-healing security framework built to pre-emptively protect DevSecOps environments. AutoGuard is a self-securing security environment that continuously observes pipeline activities for potential anomalies while preemptively remediating the environment. The model observes and reacts based on a policy that is continually learned dynamically over time. The RL agent improves each action over time through reward-based learning aimed at improving the agent's ability to prevent, detect and respond to a security incident in real-time. Testing using simulated ContinuousIntegration / Continuous Deployment (CI/CD) environments showed AutoGuard to successfully improve threat detection accuracy by 22%, reduce mean time torecovery (MTTR) for incidents by 38% and increase overall resilience to incidents as compared to traditional methods. Keywords- DevSecOps, Reinforcement Learning, Self- Healing Security, Continuous Integration, Automated Threat Mitigation

When was the last time you walked into a bank to withdraw cash? And how often do you balance your checkbook? These once routine manual processes are now primarily digital, even leading some financial giants to proclaim themselves tech companies. While many are keeping pace with consumers' demands for digital services, few organizations are implementing the advanced automated technologies that will help them stay competitive in today's digital era. Just over half (57%) of banks and credit unions started their digital transformations before this year, according to Cornerstone Advisors's "What's Going on in Banking 2021."

A popular KPI for IT services is the mean time to recovery (MTTR) -- the time it takes to resolve an incident. It is one of the most critical help desk metrics, as the longer an issue takes to resolve, the more frustrated -- and less productive -- an end user will be. Another machine learning use case in IT operations is reduced MTTR. For instance, an end user calls the help desk and complains about receiving a blue screen of death. A machine learning model evaluates that user's device data and finds the likely cause is associated with a recent Windows update. This helps the technician get to the root of the issue, and therefore solve the user's issue more quickly.

In a previous blog post, I covered some of the challenges encountered by security operations centers (SOCs) and how leveraging artificial intelligence (AI) can help alleviate these challenges, including the cybersecurity skills shortage, unaddressed security risks and long dwell times. According to ISACA's State of Cybersecurity Report, 78 percent of respondents expect the demand for technical cybersecurity roles to increase in the future. The report also mentions that the effects of the skills shortage are going to get worse. This is where AI can step in and help lighten the load considerably. During a time of tight budgets and IT spend, there is no doubt that any new expenditures must have solid business justifications.

Creates deep visibility and understanding of the landscape by learning context. Eliminates noise, predicts emergent conditions and triages issues by managing alerts. It develops and maintains a single-source-of-truth about Enterprises' IT landscape by creating an end-to-end view that connects business, applications, technology platforms and infrastructure. It filters, correlates and prioritizes alerts based on inferred risk and business impact, and aggregates and resolves alerts autonomously. It rapidly identifies the root cause of incidents, honors context-specific business policies and constraints, and prescribes the best action. It notifies stakeholders to obtain necessary consent to execute actions autonomously.

Download this complimentary webinar to learn how to use Forrester's automation framework to guide decisioning, rationalize your automation portfolio, and prepare for the future of work. In 1983, Lisanne Bainbridge (a researcher at the University of Reading in the UK) wrote the following prescient words in her widely cited paper "Ironies of Automation": "By taking away the easy parts of [the] task, automation can make the difficult parts of the human operator's task more difficult." In other words, automate all the easy things, and what's left for people to do? This maxim has never been truer. When systems become too automated, their behavior in key respects becomes harder and harder to predict and set them straight when they go wrong requires deeper and deeper expertise.

This will simplify enterprise system workings and enable "up-place" workforce to handle higher level tasks.

Real life cases in example Take the instance of a real life impact case – a top manufacturing company was grappling with error-prone and slow-paced IT. This was affecting the whole process of optimising IT resources and customer experience while leaving large and inefficient data centre footprints. To address the issue, the company implemented a true enterprise-class hybrid environment, with automated provisioning, optimised workloads across on-premise, virtual private cloud, and public cloud. The business-IT interface was transformed digitally, with a slick online catalogue to plan, provision, and de-provision resources. A customisable and dynamic web-based user experience for both users and IT staff was generated successfully, leading to reduced average server provisioning time, lower footprints, and data centre consolidation.

The Apache Software Foundation is now incubating a project backed by Cloudera Inc. and Intel Corp. that aims to bolster cyber security with Big Data analytics and machine learning. Previously known as the Open Network Insights (ONI) initiative, the project is now called Apache Spot, since it uses machine learning to sniff out bad traffic amongst good data. It can also study network traffic to characterize its unique behavior using the open source distributed storage and processing software Hadoop, which helps it to discover if any anomalies are present. Intel launched the project back in February on Cloudera's cloud computing platform. Apache Spot works by storing large amounts of information within Hadoop, then using Apache Spark to process data from deep packet inspection of domain name system (DNS) traffic, connections and log files from proxies.