Welcome back to the second part of our blog series on Deepfence's groundbreaking AI-powered remediation feature, ThreatRx. In Part 1, we introduced ThreatRx and its integration into Deepfence's Cloud Native Application Protection Platform (CNAPP), ThreatMapper. Today, in Part 2, we delve deeper into the transformative power of ThreatRx and explore its extension into runtime incident detection and remediation, along with its introduction into our enterprise platform ThreatStryker as we utilize AI to help you neutralize and remediate threats in your runtime environments.
And because we have so much to talk about in relation to the actual platform today, we have decided to split this into a 3-part series. In part 3, we will shed light on the critical need to protect AI applications and models within the cloud security landscape as the use of generative AI explodes as standalone apps and as integrated feature sets within platforms. We'll also unveil Deepfence's future-facing Generative AI roadmap, showing our evolution in how we think about the generative AI landscape, and showcasing our commitment to innovation in the cybersecurity space.
But for today, let’s dive into how generative AI plays into the runtime detection and response landscape, changing how companies think about remediation in the cloud and enhancing their capabilities to respond quicker and more effectively to threats as they see them!
The journey of ThreatRx doesn't end with cloud misconfiguration issues and vulnerabilities. We understand that in today's threat landscape, it's essential to protect your applications and data in real-time. That's why we're expanding ThreatRx's capabilities into runtime incident detection and remediation, seamlessly integrating it into our runtime workload protection solution and enterprise-grade CNAPP, ThreatStryker.
The cloud's dynamic nature demands a proactive security approach. Threat actors are continually evolving, and traditional security measures can't keep up. While we will be highlighting many of the ways AI accelerates the threat detection and response process for defenders today, we cannot ignore the ways in which generative AI will be used to help make threat actors more efficient as well. The diagram below highlights both some of the positive and negative effects of generative AI on cybersecurity.
There will always be attacks that security teams have to deal with, no matter the environment or infrastructure landscape. That is why it is critical that we get runtime detection and response right so when we do get hit with an attack (no matter how good our posture may have been up to that point) we have the right tools, capabilities, and resources to respond accordingly.
Let’s examine why generative AI is so crucially needed to help security teams solve some of the most devastating problems in detection and response. First, the median dwell time (i.e. the time a threat actor is present in an environment until it is detected) for a security incident for organizations today is 21 days. Meanwhile, in cloud environments, a threat actor often only needs a dwell time of less than 5 minutes to accomplish what they need to do. This means that mean time to detection “MTTD” and mean time to response “MTTR” are such critical metrics when thinking about cloud security. AI has the power to drastically reduce both these metrics for organizations helping them keep pace with the increasing speed by which threat actors act in the cloud.
Consider a scenario where an application running in your cloud environment suddenly exhibits unusual behavior, potentially indicating a security breach. Traditional security tools may struggle to detect this anomaly in real time. However, with ThreatRx's extension into ThreatStryker, we empower you to detect and respond to these threats swiftly with the help of generative AI.
ThreatRx leverages the wealth of security observability data gathered by Deepfence in your hybrid cloud environment. It uses advanced AI algorithms to extrapolate insights from incident data. This means that not only can we identify anomalies and security threats, but we can also guide you through the remediation process.
Organizations are faced with growing technological and correspondingly, strategic, debt when approaching modern day applications and infrastructure today. Multi-cloud environments with a variety of infrastructure modalities across PaaS and IaaS deployments, coupled with the need to maintain operational excellence in the cloud and decreasing budgets, have left the modern day CISO scratching their head trying to solve an unsolvable puzzle. Even knowing what they need to protect can be an issue. This is where Deepfence begins to simplify the process for organizations almost immediately.
As soon as you are onboarded to the Deepfence platform, we establish agentless connections to your cloud environments and quickly get a look at the cloud-level of your environment, what all is deployed, how it’s configured and what is changing as new resources get spun up or networking and/or other configuration changes are made. This quickly allows us to do enterprise and asset discovery of your environment. Deploying our agents allows us to establish greater application context for this environment. We see all inbound and outbound connections, active processes on workloads, the runtime SBOM, and are able to inspect encrypted and plain text traffic over different protocols such as http/s and DNS. All of this seamless discovery, helps us establish a rich understanding and visual graph of the topology and landscape of resources we are protecting.
Next, we are able to scan that environment for different types of risk that might be present (vulnerabilities and CVEs, exposed secrets and sensitive data, malicious software or behavior, and/or critical misconfigurations at the cloud and workload level). This allows us to understand different types of critical attacks (OWASP Top 10, zero-day vulnerabilities, etc) but also identify the tactics, techniques, and procedures (TTPs) that threat actors would utilize within the environment to exploit the environment. We can then perform exploitability analysis using the runtime context we have about the environment by overlaying it on the results of the static risk scans. This builds out a clear ThreatGraph of the internal attack surface that shows which attack paths have both SEVERE & EXPLOITABLE risk on them, helping security teams rank and order threats to their environment by prioritizing ACTUAL vs POTENTIAL risk in remediation efforts.
Lastly, we can detect and respond to threats along those critical attack paths - quickly identifying anomalous behavior and giving you immediate guidance, with the assistance of ThreatRx, to neutralize those threats and adjust your security posture so an attack like that cannot occur again. Imagine receiving a real-time alert that your application's behavior seems suspicious. ThreatRx can provide immediate remediation suggestions based on the current runtime context. It can advise you on isolating the affected component, rolling back any unauthorized changes, and implementing additional security measures. This is what Deepfence’s ThreatRx enables in the remediation process!
By integrating AI-powered remediation into runtime protection, we're enhancing your ability to secure your cloud environment proactively. It's not just about identifying threats; it's about taking swift and precise action to neutralize them.
In conclusion, the future of cloud security is AI-powered, and Deepfence is at the forefront of this transformation. ThreatRx's AI-assisted remediation is just the beginning, with runtime incident detection and response forever being altered for threat defenders with the assistance of generative AI. Stay tuned for the next chapter in the evolution of cloud security with Deepfence when we dive into our Generative AI roadmap in Part 3 of this blog series. Together, we're securing a future that's agile, proactive, and AI-driven.
Watch the ThreatRx demo at https://go.deepfence.io/threatrx-webinar
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