Hello everyone and thank you for joining today. Today I'll be presenting on harnessing AI for autonomous threat defense in multi-cloud security. My name is Pip Kura. I'm principal architect at Trace three. Today we will explore how artificial intelligence is transforming security in multi-cloud environments, helping us defend against increasingly sophisticated cyber threats. Let's begin by acknowledging the reality we are facing today. In cybersecurity, the threats aren't what they used to be. Legacy security frameworks, things like basic firewalls, VPNs and intuition detection systems. These were designed for a old where threats were simpler, slower, and frankly easier to spot. Today's threats are incredibly dynamic. We are seeing polymorphic malware that changes its code every time it executes. We are facing advanced persistent threats, attackers that sit quietly inside networks for months learning, adapting, avoiding detection. And let's be clear, these not the work of random hackers anymore. It's highly organized, often state sponsored, and increasingly automated. Machine learning is now used by attackers themselves to develop smarter, faster exploits. One of the biggest problems, isn't it? The gap between. When an exploit is created and when it's launched, it's shrinking. Organizations barely have time to patch these vulnerabilities before attack a strike. The old models of detect, done, respond are too slow. We need a fundamentally new approach, and that's where AI begins to shed. And now let's talk about the AI security advantage. AI brings three, transform two capabilities to cybersecurity, and it's important to understand them individually. For the first, let's talk about real time analysis. We all know AI can process petabytes of security data such as logs, network traffic, user behaviors within milliseconds. It's like giving your security operations team a super human vision. Patterns that would take a human analyst days to recognize or surfaced instantly. Second is autonomous response. Historically, once an alert was raised, humans had to analyze, validate, escalate, and finally act that takes hovers or longer. In the meantime, the attacker is moving, stealing, and damaging the. Enterprise Now, AI flips This. Systems now detect threats and immediately act by isolating, infected missions, shutting down dangerous processes often without needing a human to approve it First, third, and perhaps more most exciting is adapt to defense AI based systems or self-learning every attack they see. Every anomaly they process makes them smarter. They evolve alongside attackers. Traditional signature based defenses, a static AI based defenses are alive and growing stronger every day. When we talk about measurable improvements using ai, we are talking about operational realities, not theoretical pro promises. Think about false positives. In many organizations, security teams are drowning in alerts. 90% of those alerts, they're just noise by reducing false positives by 30%. AI doesn't just make teams more efficient. It helps them focus on the right threads faster with less burnout. Response times are cut by 50%. Means incident that used to take six hours to mitigate now takes under three minutes. In cybersecurity, speed is everything. Every second, an attacker remains inside your network, increases damage exponentially and a 40% faster. Incident resolution rate reduces not just the duration of attacks. But also their downstream costs, like regulatory fines, lost customer trust and recovery efforts. These numbers translate into real business value, not just better security, but stronger bottom lines. Now let's talk about AI powered behavioral tactics. Traditional security often relies on static rules, for example. If X happens or Y happens, block it. The problem is attacker knows the these rules and they find ways around them. But now let's see it. The ai, it takes a different approach. AI watches it and learns from it. It knows what normal looks like, not based on rigid definitions, but based on continuous observation. For instance, or for example, if a user always logs in from Austin, between 9:00 AM to 5:00 PM and generally there's a login from Singapore at midnight that's flagged immediately. Or for instance, if a database starts sending unusually large amounts of sense to data outside normal business hours, AI detects that and crucially, it doesn't just flag anomaly, it takes action. It takes action by automatically locking those suspicious accounts or isolating those suspicious devices. A prevents minor anomalies from becoming major incidents. Let's talk about zero trust implementation. I believe this as being has become a buzzword, but it's also a fundamental principle. It's like never trust, always verify. That's zero trust implementation, right? So we can no longer assume that because a device is inside our network, it is safe. We must verify everything like every user, every device, every access request least privilege means thinking critically about access. Does this really, does this user really need database segment privileges, or can they just read reports? And assuming breaches means building layer defenses like microsegmentation. Microsegmentation is nothing but dividing the networks in enterprise into smaller zones, which limits that blast radius if an attacker breaks through. Now with ai, it enhances zero trust strategies by monitoring trust continuously. Not just at the moment of login, but during the entire session. Let's talk about AI integration security platforms. AI enhances existing security platforms within multi-cloud. Think about a typical SIM platform. S sim means security, informa information and event management. Traditionally what it does is it aggregates logs, normalizes data and triggers alerts based on rule sets. But when you add ai, something powerful happens. AI can correlate security events across systems automatically. For example, it might see a strange authentication attempt in Azure active. Which correlates with abnormal API calls that is happening in AWS cloud, and tie that to a such specious download that happens in a GitHub report. Certainly what looked like isolated events. Now tell the story of a coordinated attack. Similarly in so platforms, which is security orchestration, automation and response platforms, AI helps automate this instance of handling these workflows. Now for the Imagine This, a phishing email is de detected. AI automatically quarantine. The email locks affected user account flags, similar emails, and notify security analysts. All within seconds. Time is the key. And this all comes with no manual ticket triage, no waiting hours for investigation it. This is fast orchestrated with intelligent action. And finally, AI enriches alerts with contextual intelligence. Instead of just saying anomaly predicted, the alert might include more details. For example, this credential matches known tactics, techniques, and procedures of the advanced threat groups. For example, like a PT 29 and. Qualifies them as medium severity, or it would even take immediate action as recommended. This kind of context reduces guesswork for analysts and accelerates time to action. Now, let's talk about task platforms. Cloud security as organizations expand across AWS, Azure, GCP, and multi-cloud environments, and even in hybrid environments. The complexity of managing security growth exponentially, there is no longer a single perimeter to defend your applications. Data APIs and users are scattered, are across dozens of services and providers. Thankfully, these major cloud platforms have begun integrating AI into the security ecosystems. In AWS services like Guardian AI continuously scan cloud trail logs for unusual API activity. Imagine detecting an administrative API call happening at 2:00 AM from a country where you have no employees. In Azure, the security center uses mission learning to a assess these vulnerabilities across compute, storage and identity services. Which predicts potential breach points before attackers can exploit them in Google Cloud Security Command Center applies a two and more importantly, to monitor containerized workloads. Given the rise of Kubernetes and microservices, protecting containers which are dynamic and affirm is critical. Now what all ties all this together is that YAP. AI operates at cloud speed analyzing millions of data points across platforms that would or own human teams. When security is consistent, automated and intelligent across clouds, it allows organizations to innovate confidently without sacrificing risk management. Yes. Let's bring this all to life with real world examples. Financial services are high value target for attackers, as we all know, especially with the rise of FinTech. But now with FIN FinTech companies, banks can now deploy ea not just for fraud detection post-transaction, but to prevent fraudulent authentication before it even succeed. For instance, one of the major institution implemented AI powered authentication, anomaly detection, which prevented them over 4.3 million in losses in just in a single quarter. Think about the ripple effect, not just money sale, but brand and trust are preserved. Similarly in healthcare faces different but equally critical challenges. Patient data is among the most sensitive information in the world protected by regulations like hipaa. Hospitals now use AI to monitor network traffic for ransomware behavior patterns. The moment AI detects later movement or encryption behaviors associated with this ransomware, it isolates affected endpoints automatically sometime before the ransomware fully executes. And in e-commerce companies face relentless DDoS attacks, especially during peak season, like holidays or major holiday sales. Using AI, they can analyze real time traffic patterns to distinct use legitimate customers from board driven traffic. This allows them to mitigate attacks without disrupting real shoppers. Maintaining revenue flow, and also most importantly, the customer satisfaction. So where are we headed? What does the future of cybersecurity looks like? When AI takes center stitch, one major shift is towards brick two Defense. Rather than just reacting to threats and that have already materialized, future AI systems will anticipate attack patterns based on early signals such as subtle network behaviors, metadata anomalies, or threat intelligence. From around the globe, AI will model the most likely paths an attacker would take and proactively close the vulnerabilities before exploitation happens. We'll also see more self-healing infrastructures. Imagine systems that upon detecting compromise, automatically roll back to a secure snapshot or reconfigure themselves to eliminate infection vectors without even human intervention. Finally, with growing compute, quantum computing maturity, traditional encryption algorithms will be increasingly vulnerable. Yay will be critical in managing adaptive quantum resistant encryption strategies, evolving defenses in real time based on quantum threat levels. It's not just about detecting and responding anymore, it's about preempt strengthening defenses faster than advisories can adapt to. To wrap up, let's review the key takeaways from today's session. First. AI is not optional anymore. The scale, speed, and sophistication of modern threats require defenses that are equally dynamic. Second, the results are measurable. Organizations that integrate AI seek quantifiable improvements in reduced false postures, faster detection, faster response, and lower breach impact. Third you to start your journey strategically. Begin with AI enhanced monitoring and instant detection. Build muzzle there. Then gradually move forward towards fully autonomous response capabilities, and finally, invest in your people. AI is not a magic box. It's a force multiplier for skilled security professionals. Develop teams that can work symbiotically with ai. Analyzing complex situations, providing ethical oversight and refining AI models with domain expertise. Together, AI and human intelligence are the future of cybersecurity. Thank you again for spending time with me today. I'm excited about the feature and I hope you are too. Thank you.