Transforming to AI-Assisted Continuous Security – DevOps Experience 2024

Hey, welcome everybody, and, uh, thank you for joining today's session with Textron. And we're gonna talk about transforming to AI assisted continuous security. Uh, in this presentation, I'll be diving deep into how AI can revolutionize the way we approach continuous security across development and operations.

The goal here is to explore how, uh, you can enhance security automation, enable faster detection of vulnerabilities, and also improve incident response. I mean, by the end of this talk, I hope to provide you with some actionable insights on how your organizations can transition from traditional DevSecOps and SecOps models to an AI assisted continuous security approach that's more efficient, proactive, resilient, and, um, basically better. So, let's get started.

So, I'm honored to be here. Uh, a little background about myself. I've spent many years, uh, in qa, DevOps, DevSecOps, and SRE.

I think I earned the moniker of DevOps the Gray. I've been flam, I've been doing this type of work for more than 48 years, um, and worked in various roles in corporations and so on. And the latter part of my career, I've been performing as a strategic transformation consultant, helping enterprises, institutions, service providers, and, um, manufacturers actually provide, uh, these s these practices and adopt, help them adopt these practices.

I've also had the privilege of being recognized as an outstanding IEEE engineer and, uh, contributing as an author on various topics like DevOps containers, testing security. com containers, uh, sorry. com.

I also teach courses regularly, uh, for the partners of the DevOps Institute and People Cert. Today, I'm excited to share my insights on how AI can transform continuous security practices. So the presentation, uh, basically has three core objectives.

Given the limited time, that's enough. First, you'll gain a clear understanding of what continued security is and why it's crucial to modern DevOps and SecOps practices. We'll also explore how to transition from the traditional sometimes siloed models of DevSecOps and SecOps to a more unified approach using continuous security.

Uh, we'll also discuss how AI plays a vital role in this transformation today, helping to automate and assist in threat detection, incident response, and overall security integration. Uh, the ultimate goal is to bridge the gap between development and operations and security teams, while leveraging AI to make security processes more efficient and, uh, effective. There are stark differences between DevSecOps and SecOps.

You know, I attended some RSA conferences recently, and I always find one to go to any, you know, vendor booth. You have to sort of say, are you DevSecOps or are you SecOps? So that alone tells you that there's differences.

You know, the challenges are, you know, do the various cultural and operational silo, uh, DevSecOps prioritizes rapid, you know, software delivery, focusing on CI/CD, pipeline automation, things like that. Well, SecOps emphasizes stability, risk and compliance, uh, in production and monitoring detection, incident response, uh, more in the production side. Uh, the lack of a cohesive strategy that binds these two teams is often caused by misaligned goals, fragmented tools, KPIs that don't overlap, and this disconnect is fi is further, you know, exacerbated by legacy structures, insufficient training, slow adoption of integrated security tools to truly secure environments, these teams need to align their tools, their data, and their communication strategies under one cohesive framework.

So what is ai? Continuous security? ai, continuous security focus on applying ai, augmented security practices across the entire software development and, and operations lifecycle.

In DevSecOps, the goal is to prevent vulnerabilities during planning engineering, and in the CI/CD pipelines and deployments in ensuring that security is integrated from the start. Whereas in SecOps, the focus shifts to defending against exploits and attacks and production environments. AI assisted continuous security provides real-time threat detection, automated security testing, and a seamless integration of security measures across both development and operations.

This ensures that we progress from development to production and, uh, security remains a constant proactive, you know, uh, element throughout. So let's compare briefly the traditional approaches of DevSecOps and SecOps. With AI assisted continuous security and DevSecOps, the focus is shifting, uh, security labs, integrating security measures into the development and CI/CD pipelines ensuring vulnerabilities are caught early.

SecOps, on the other hand, is concerned with in production security, focusing on monitoring, protecting live systems from exploitation. AI assisted security goes a step further by applying end-to-end security practices continuously securing every phase from development through to production by leveraging AI and machine learning. The key advantage of AI here is the ability to reduce complexity, lower the cost, and minimize resource requirements, allowing organizations to enhance security coverage while maintaining efficiency.

I mean, frankly, uh, the combination of, of things involved in continuous securities would, uh, is relatively complex, if not assisted by ai. So AI is essentially offering an opportunity to, you know, address this issue that I'm talking about here that otherwise would be actually difficult to, to deal with, not to say that security isn't difficult to deal with in, in any case. Um, so, and let's look at a use case.

You know, if we go back and think about what happened with the MoveIt supply chain ransomware attack back in May, 2023, the seal up, um, ransomware gang exploited a zero day vulnerability in, in the move it file transfer software to steal sensitive data. The costs were enormous. You know, there's various estimates, but you know, up to 500 million is, is actually believable across, you know, the globe with impacts across financial services, education organizations, government institutions, the attack highlighted the critical need for proactive security measures.

As DevSecOps forces focus on vulnerability prevention. While SecOps handles exploit defense, the failure to implement patches and address new vulnerabilities immediately led to widespread co corruption or disruption. This case illustrates the importance of continuous monitoring and enhancing security throughout the software lifecycle, leveraging both DevSecOps and SecOps, you know, more intimately to prevent such breaches in the future.

Yeah, we could look a little deeper and say, well, how could AI assisted continuous security have helped play a, you know, a positive role in mitigating the, and maybe preventing or, or certainly mitigating the move its supply chain ransomware attack. First of all, with AI driven continuous vulnerabilities section, the platform could have identified the SQL injection vulnerability earlier, even before it was exploited. Uh, AI can scan and test code in real time flagging vulnerabilities automatically suggesting patches.

In this case, rapid patching would've, you know, been accelerated as AI can orchestrate the deployment of, of patches across multiple environments simultaneously. Beyond patching proactive threat model monitoring, powered by ai, uh, could have provided more realtime alerts, allowing the organization to respond to potential threats before they escalate. And also, AI can enhance the effectiveness of system hardening and, uh, security chaos engineering experiments by continuously testing security defenses, making them more, you know, resilient to change.

So there's plenty of other cases. I mean, these are just, again, some examples that where AI assisted continued security might have helped. When you look at, for example, the solar wind supply chain attack log four vulnerabilities, Equifax breach, we see, you know, many of these incidents occurred due to gaps between DevSecOps and SecOps.

Certainly you could say that, uh, upon analysis in the SolarWinds breach, for example, attackers exploited weaknesses in the software supply chain. If AI assisted continuous security had been applied, continuous testing and patching could have identified the tampering much earlier, or the log four J incident. AI assisted tools could have flagged vulnerability versions in the library and automatically patched systems.

As soon as the vulnerability was disclosed. Uh, the Equifax breach highlighted the importance of continuous monitoring. AI tools could have provided ongoing scans and threat detection that would've alerted systems and teams, uh, to unpatched vulnerabilities.

In all these cases, AI assisted continuous security would integrate both DevSecOps and SecOps practices more closely, enabling more real time threat protection against such threats. There are a number of, uh, situations where AI assisted continuous security practices are ideal, um, and would be absolutely valuable. For example, in large enterprises where DevSecOps and SecOps teams often operate in silos, AI enables automated collaboration and coordination ensuring the securities embedded across both development and production.

Uh, other cases, you know, where software suppliers are separated from their clients, uh, AI assisted continuous security enables threat monitoring, ensuring the software is secure even as it integrates with the customer's environment, where government institutions and military applications where sensitive information is at stake. AI can manage continuous compliance checks. Real-time threat detection, security policy enforcement, uh, network infrastructure is another interesting case where software manufacturers are disconnected from the network service providers.

ai, you know, ensures both the software and the network can be secured in tandem. And industries like finance, healthcare, critical infrastructure where security breaches can have catastrophic consequences. AI assisted continued security provides the continuous protection needed to meet regulatory requirements and defend against ever evolving cyber threats.

Over the past five years, we've seen a significant increase in real world impact of security breaches. Uh, for example, ransomware payments have surged at least by some reports more than 171%, and, uh, cybersecurity insurance premiums increase more than 96%. Uh, those numbers vary depending on which report you look at, but those are some examples.

The costs of forensic investigations, investigations that are also reaching millions of dollars per breach. You know, organizations are facing major lawsuits from their all, from their clients, their significant reputation damage, prolonged business losses, often up to six months reported. Uh, compliance penalties have also risen a lot, uh, as regulatory, uh, intensifies and long-term organization disruptions have become a reality.

All of these trends demonstrate that organizations need a proactive AI assisted continuous security approach to prevent costly breaches and minimize damage. By adopting AI driven security practices, organizations can stay ahead of evolving threats and reduce the financial, legal and, uh, reputational damage on top of that. Today, you know, we're facing a new wave of cyber attacks where AI is playing a central role.

Criminals are using AI driven phishing attacks that dynamically adapt and improve, making them harder to detect polymorphic malware, such as deep blocker uses AI to change its behavior and evade traditional security measures. We're also seeing the rise of AI generated fake media, like deep fakes, which are being used for fraud and extortion. Uh, ransomware attacks are becoming more sophisticated with AI helping to evade detection by continuously altering attack patterns.

And criminals are also leveraging botnets for AI, for command and control operations, making these attacks more coordinated and harder to shut down. So organizations do need AI assisted continuous security to match these advanced AI based threats and ensure that they're well protected. Okay, so what is, you know, the how of ai, uh, assisted security let's, I, I break down, uh, as I have with other, uh, things in my various publications and work different pillars of practices.

So actually, this is, uh, part of a book that I wrote recently called, called Continuous Testing Quality Security and Feedback. Um, so to implement AI continuous security, we need to focus on eight key pillars. Uh, they include establishing continuous security culture where security is integrated into every process and decision, uh, continuous security awareness and training, ensuring that employees understand security risks and best practices, security integration across the life cycle, making sure that the security is embedded at every stage of development and deployment.

Automated continuous security testing helps identify vulnerabilities before they can be exploited. Uh, we also need proactive risk management, continuously assessing potential threats and taking preventative measures. Uh, rapid incident response capability ensures that organizations can quickly detect, respond, and mitigate security incidents.

And finally, continuous security monitoring and compliance and continuous security feedback and improvement help ensure that security practices are up to date aligned with regulatory requirements and are constantly improving. So again, these are pillars of practices, and underneath each of them, there's, you know, a series of specific, uh, practices for a well-engineered ai, such a continuous security. So by bottom line here is, you know, how can suppliers and customers transform to AI assisted continuous security?

That's what the most of the rest of the presentation will explain in high level terms. Um, to successfully implement AI assisted continuous security organization need to focus on strategic transformations, and that begins with the vision and goal alignment. This includes setting clear objectives, aligning security goals with business priorities.

Next, we conduct strategic assessments, which involve discovery surveys, gap assessments, current state value or mapping to ensure a baseline. From there, a comprehensive strategic plan is developed, and this plan includes future state value stream maps, uh, themes, uh, tool selection and a roadmap for implementation. Governance and monitoring tools ensure accountability throughout the transformation, and the entire process is accelerated by leveraging AI assisted tools to analyze results, provide insights, guide the implementation fundamentally by following these, this is a strategic blueprint.

Organizations can achieve a secure AI driven future, I must say. Uh, you know, whenever I see organizations not following such a logical, logical path, their transformations tend to fail, or at least go off the rails, or at least not meet their expectations. I mean, this is not a, you know, complex prescription, but it's particularly important.

So I offer AI assisted continuous security gap assessments, uh, to align with teams and help define the solution priorities for the teams. AI assisted team alignments ensure that security responsibilities are clearly defined across DevSecOps and SecOps teams. In this approach, AI helps by analyzing each team's gaps and suggestions for, for specific practices to address the highest priority gaps.

And that way, you know, you can have a solution that is fit to the organization. The next step involves solution mapping, where AI can assist in analyzing the more critical vulnerabilities and risks, and then building a roadmap where, um, you know, for implementation, basically, uh, using ai, you can assure these alignments and priorities are based on real time data and decisions that are made with strategic focus. By the way, you can download a copy of my AI assisted continuous security tool for free from my website, along with a whole lot of other tools that I offer for free on my website.

Uh, value stream mapping is particularly valuable, pardon the pun, uh, for this as well. You know, the value stream, uh, mapping process, uh, helps you understand where security gaps occur throughout the software development and, uh, lifecycle. And coupled to deployment, all AI assets, uh, provide a, you know, a clear visual representation of your current processes, highlighting and efficiencies and delays, and, uh, security implementation.

AI really helps with the analysis and help you build, you know, current and future state, uh, mappings. The map serves as a foundation for baseline, where AI helps determine the bottlenecks and metrics for security performance. The most exciting part is that AI allows for the design of a more efficient future state, where security is fully automated and integrated across all stages of the lifecycle.

And this way, you can measure and continuously improve your security posture. Uh, the process is supported by tools that are available to download, uh, are are available to download again by my website for value stream mapping as well. After you have a strategic roadmap, you know, you need an implementation roadmap for AI assisted container security, which is divided into four key themes.

Theme one is all about preparing your AI platform test environments and tools. It also includes metrics and workflows that will be used to monitor and track progress. Theme two, uh, focuses on standardization, migrating applications to standard pipelines, informing centers of excellence for continuous security training.

Theme three expands the current security coverage to include additional applications and advanced training. Uh, finally in theme four, we optimize security processes by accelerating test creation, ex execution and analysis. Each theme builds on the last to create a fully automated and scalable ai, uh, assisted security environment.

There's plenty of tools out there, although none were specifically designed for AI assisted continuous security. But when it comes to, you know, implementing AI assisted continuous security, there, you know, there's no one size fits all. Solution.

Organizations need to choose the right set of tools based on their specific needs, you know, that were determined by the prior, you know, uh, assessments and so on. Some excellent examples include Microsoft Azure, uh, Sentinel, which provides AI driven threat detection and response capabilities in a cloud native environment. Uh, Palo Alto Networks, uh, Prisma Cloud offers comprehensive cloud security across applications and infrastructure with AI assisted threat detection.

Splunk enhanced with AI powered insights delivers real-time analytics and automated, uh, threat responses to help monitor and secure large environments, um, tools like Darktrace leverage AI to detect advanced threats in real time learning the unique behaviors of the system to provide proactive defense. GitHub, advanced security and GitHub pilot copilot, I should say, allow, you know, developers to integrate AI assisted security directly into your CI ICD pipelines, catching vulnerabilities earlier in the process. There's other strong options that are listed.

IBM QRadar, uh, for seam solutions, rapid seven insight VM for AI assisted vulnerability management. Tenable with predictive prioritization to automate and streamline vulnerability management. The point here is that, you know, you need to tailor your tool set to the specific demands of your particular infrastructure and security needs by leveraging ai, and certainly don't, you know, just build all your own tools.

There are plenty of them out there, but the tool selection process is really, really critical. Um, alright, so platform engineering is a hot topic these days. It's really not a new concept actually, but it's a valuable concept.

The idea of providing a simple to use portal for stakeholder gr greatly simplifies access and use of the many tools and tech stacks needed for day-to-day tasks required for continuous security. A platform engineer specifically for AI assisted continuous security brings together automated and AI enhanced capabilities into a centralized environment that manage security across the entire, uh, software lifecycle. Uh, platform provides continuous monitoring, automated compliance checks, uh, proactive incident response.

One of the greatest advantages here is the scalability that it offers. Helping organizations stay ahead of emerging threats by enabling real time detection, mitigating, all while aligning development, operational security teams offering a more secure, efficient and scalable posture. Three types of security.

Me, you know, continued security metrics are relevant, uh, to track progress of your AI assisted security, uh, information transformation. We look at the percentage of automated security checks in CI/CD pipelines and compare vulnerabilities detected pre and post-production, meantime to detect incidents, um, collaboration metrics. Then there's a set of metrics for effectiveness of the solution once you have it, which is, includes things like meantime to repair, reduction in security incidents, false alerts, compliance and costs.

And finally, business mission level metrics such as downtime due to security breaches and security of spending as a percentage of budgets are crucial. Uh, so overall, these metrics are vital in demonstrating how AI assisted security can improve security outcomes as well as business performance. Yeah, um, the whole point, I guess, is to close risk gaps, right, that arise from silo DevOps and SecOps practices.

Some of the key gaps include inconsistent or delayed threat detection manual processes that lack automation, fragmented security postures and development. There's many others. Uh, but fundamentally, by leveraging ai, you can achieve continuous monitoring, realtime detection, automated, you know, and automation across the entire lifecycle.

As you move to, uh, you know, through, through transformations and implementation, there's plenty of things that you can do wrong, and this is why you have to, you know, know what you're doing. Uh, otherwise you can journey your way into a pitfall. So these are just examples of pitfalls to avoid.

And some of the, you know, strategies for avoiding, and I'm just an example for, you know, a lack of team alignment can lead to delays. So it's really critical to share goals and hold regular cross team security standups. Um, there's a list here.

We're not gonna go through the whole list, but you can review it later if you wanna review the presentation. Bottom line is AI can help by continuously feeding back security data or ongoing optimizations and help you avoid some of these common pitfalls. Uh, you need to keep motivating teams, not just the first time through a transformation, but ongoing as well, because just 'cause you have a solution doesn't, you know mean you're gonna, it's gonna keep working.

So these are some suggestions on how to keep motivation working, uh, and, you know, focus on the benefits, demonstrate value with ROI. Examples, developing skills, again, this is a longer list, but you get the idea. And, uh, generally speaking, this approach will help ensure AI driven transformation succeed.

Not only, you know, the first journey, but also on an ongoing basis as well. There are plenty of advantages of AI assisted continued security. First of all, we see security teams improving safe deployments, enhancing threat detection across the life lifecycle, providing faster and more accurate responses, reduce false positives, having more efficiency and vulnerability management, adapting security automation.

And again, the list goes on here, but in general, this leads a significant reduction in overall costs, while at the same time optimizing reduced allocation and essentially frustrating the bad guys like it shows in that little diagram there. Okay, just think for a moment about the long term, and here's where, you know, I really put on my, you know, my crazy hat and think about where could this ultimately end up. You know, having been involved in DevOps and security for many years, um, I have a dream, which I think is becoming more feasible, largely because of ai, uh, that, you know, we may end up one day having something instead of DevSecOps or SecOps, it'll be dev sec, dev in ops model where AI becomes more integrated into security practices.

Traditional distinctions between development, security and operations will blur. It'll be, you know, because of faster lead times, continued delivery, you know, DevOps is causing organizations to go faster and faster and faster. The idea of shifting left is almost becoming irrelevant as you get fast enough that, you know, there's no left to shift too.

Uh, so you're already pushing security towards faster deployments. And, uh, ultimately, you know, with, with all the, uh, uh, automation, you know, predictive analytics, automated security measures, and a AI edit decision making, uh, and things like feature flag rollouts, if you can get those coordinated across, you know, the, so the, um, supplier client boundaries very well, uh, then ultimately you may get to the point where, you know, maybe in five years, I don't know, uh, where you can actually do development with security, security development in operations, uh, safely. These are the main takeaways.

Um, AI bridges the gap between DevSecOps and SecOps by providing real time monitoring, automated vulnerability management, proactive threat detection, all the other things that we mentioned in the talk. The use of AI and machine learning is essential to scale these practices, reducing error and address some emerging threats. Honestly, I'm not sure this is feasible without, you know, help from ai.

AI also drives proactive, uh, defense enabling organizations to shift from reactive security models to predictive. Ultimately, AI assisted automation can deliver greater scalability, efficiency and adaptability, um, helping security frameworks consistently evolve to meet the ever, you know, changing threat landscape. Bottom line is I do encourage you to embrace AI assisted continuous security as a concept and help make it a reality to stay ahead of, you know, modern cyber threats that are also taking advantage of ai.

com. com. Uh, thank you.

I appreciate your attention and I hope you enjoyed, uh, this and learned something from it, and hope to hear from you. Thank you.