Hey everyone. I'm Portola Senior product Security engineer, and today I'll be talking about how machine learning is transforming security in DevOps pipelines for cloud native applications. We are facing a critical inflection point in cybersecurity with 94% of enterprises now using cloud services and AI powered cyber attacks. Increasing by 63% since 2022. Traditional security approaches simply can't keep the pace anymore. What I'll share today is how the machine learning algorithms integrated into DevSecOps. Pipelines are revolutionizing the cloud security while allowing the organizations to maintain rapid development cycles. The data is compelling that organizations using machine learning powered security tools experience 71% fewer security incidents and deploy the secure code three times faster than those using the conventional security models. Now let's dive into the changing security landscape. Let's first understand that evolving security landscape that's driving this transformation, we are seeing an unprecedented. Cloud adoption stage with 94% of enterprises now using cloud services, this creates complex security challenges across increasingly distributed architectures. At the same time, cyber attacks leveraging artificial intelligence have increased 63% since 2022. Outpacing the traditional defense mechanisms, the attackers are getting smarter and we need to match that intelligence. This creates what I call the security development gap. Conventional security models simply cannot match the speed of modern development cycles, creating the dangerous vulnerability windows. And the good news is a new defense paradigm has emerged machine learning enhanced security tools provide a 71% reduction in security incident while enabling the three time faster deployments. This is the transformation we'll explore today. Now coming to the machine learning enhanced security tools performance metrics, the data behind machine learning enhanced security tools is compelling. Let me walk you through the key performance metrics. The organization's implementing machine learning powered security tools have witnessed dramatic improvements across the key performance indicators. The most significant gains have been in container security with 89% reduction in vulnerabilities and the meantime to remediation, which decrease from 38 days to just over four days. That's an 89% improvement. These are in the theoretical numbers. These metrics are based on the data collected from over 500 enterprise implementations. Notice the development speed bar. It's off the charts because these tools enable security to become an accelerator rather than a blocker. So supervised learning for vulnerability detection. Now let's look at these specific machine learning approaches. Starting with supervised starting with the supervised learning models that are trained on mass data sets of known vulnerabilities, code patterns, and the associated risks. These models learn to identify similar patterns in new code flagging potential security issues before the deployment. The crucial advantages that by continuously learning from new vulnerability data, these systems improve detection accuracy over time and adapt to the emerging threat patterns. Let me share a concrete implementation example with Snyk In 2024 Snyk Machine Learning enhanced scanning detected 78% of vulnerabilities before the production compared to just 31%. With traditional static analysis tool, the tool is analyzed both application code and the dependencies. The integration into Series CICD pipelines allows for automatic vulnerability detection, identification, using commit and build phases, allowing. Developers to address the issues immediately. Now, let's dive into the unsupervised learning for an anomaly detection. While supervised learning is excellent for non threats, unsupervised learning excels at identifying previously unknown threats by detecting deviations from the established behavioral patterns. This approach involves four key elements, which is behavioral analysis, identifying the patterns outside normal operational parameters. The baseline establishment, creating normal operation profiles across the environments and the continuous monitoring, the real time inspection of applications and infrastructure behavior. And the last one is self-learning models adapting to evolving the environments without explicit programming. Unlike the rule-based systems, these models can spot novel attack vectors and zero vulnerabilities by recognizing when the systems behave abnormally. This approach has been particularly effective for runtime threat detection in container environments. So the machine learning, enhanced container security. Speaking of containers, organizations implementing machine learning, enhanced container security solutions have reduced vulnerabilities by 89% compared to the traditional approaches. This comprehensive prediction covers the. Entire container lifecycle from build to runtime and ensuring that micro microservices remain secure without sacrificing the development velocity, the production span. So like four critical areas. First thing is image scanning. Machine learning analyzes the container images for non vulnerabilities and suspicious patterns. Second one is the runtime protection. The behavioral analysis identifies the anonymous container activities in real time, and network security monitors the container communications for unexpected connection patterns, and the configuration analysis evaluates the configurations against the security best practices. So natural language processing for ISE security infrastructure score has revolutionized cloud deployments, but it also creates a new security challenges. Natural language processing techniques have now transformed ISE security by understanding the semantic meaning behind cloud configuration files. So this approach enables security systems to identify the. Misconfigurations risks that traditional rejects based scanning might miss, such as overly permissions, the access policies or insecure default settings in cloud resources. The NLP approach involves like four steps, which is the code analysis, the passing infrastructure code to understand resources and the relationships. Context understanding, grasping the infrastructure intent beyond the simple pattern matching and the risk identification. Identifying the mis configuration risks and compliance violations, remediation suggestions, providing the code level fixes for identified security issues. Now let's dive into the machine learning augmented CICD security pipeline. When we bring these technologies together, we get comprehensive machine learning, augmented CICD security pipeline that covers every stage of development in the code. Commit stage, pre-commit hooks, the machine learning powered code analysis to catch vulnerabilities before they enter the code base during the build stage, container measures and dependencies are scanned by supervised learning models for non vulnerabilities in the test phase. Automated security testing users, machine learning to generate test cases that target likely vulnerability points at the deployment stage. Infrastructure scanning with NLP ensures secure cloud configurations before provisioning during the runtime. Unsupervised learning models monitor application behavior to detect anomalies and the potential breaches. Organizations implementing machine learning. Augmented CI CD pipelines reduced meantime to remediation from 38 days to just over four days, and a dramatic 89% implement in vulnerability response time. This acceleration stems from earlier detection and more accurate vulnerability information. Now let's deep dive into a case study. So a financial service organization implemented one of the security measures. Let me share a real world case study. So a leading financial services company struggled with releasing updates to their mobile blanking platform, which secure peer reviews taking an over like 27 days. This created a significant challenge for their business after implementing and machine learning security pipeline that cloud con. The cloud that could prioritize vulnerabilities and automatically generate full request for common issues. They reduced security review time to just two days while simultaneously improving their overall security patterns. Most impressively, their automated remediation system now handles 76% of common security issues without human intervention, allowing the security team to more focus on more of the complex threats and the strategic. Improvements. Now let's deep dive into the implementation framework. If you convinced and want to implement this approach in your organization, here's a practical implementation framework. Start with assessment and planning. Document the existing security controls and gaps. Define the success metrics aligned with the business goals. Build a cross-functional implementation team. Then move on to the initial implementation. Implement the code scanning in the CI pipeline. There are a lot of tools in the market that we could use. Deploy the container security monitoring, establish the feedback loops for model improvement. Finally, advance to advanced integration. Deploy unsupervised models for runtime protection in form of automation. Implement the automated remediation workflows, create customized machine learning models too. For the unique environments. Key takeaways and next steps. Let me conclude this with a key takeaway from our discussion today. Performance advantages. If we imp if we are able to implement this, we will likely be having 71% fewer security incidents, three times faster security deployments close to 90% reduction in M TTRs. Which is meantime to remediate the vulnerabilities from like 38 to just over four days, 43% lower security remediation costs, and the implementation strategy, as I discussed earlier. Start with the supervised learning for non vulnerabilities layer in the unsupervised models for anomaly detection. Implement the across entire pipeline from code to runtime. Establish. Machine learning model evaluation metrics to use by 87% of high performance teams. Next steps for you after this presentation is to just assess your current security automation maturity, identify high impact integration points in your pipeline. Start with one machine learning security tool and measure the results. Build internal expertise through hands on implementation. By integrating this machine learning into your DevSecOps pipeline, you can achieve the same contribute goals of enhancing security while accelerating the deployment and the development. The key to implement machine learning power tools strategically across your entire development lifecycle. So that it'll allow you to shift from reactive security measures to predictive protection. Thank you all for paying attention to this. Thank you. Have a great day.