Today, we’re joined by Rupert Marais, an in-house Security Specialist whose work focuses on the sharp edge of cybersecurity: endpoint protection, strategic defense, and advanced network management. In our conversation, we will explore the shadowy world of modern, fileless attacks that traditional security tools often miss. We’ll delve into how attackers use trusted system tools to remain invisible, the unique challenges of securing fast-paced developer environments, and why an AI-powered, zero-trust approach is becoming essential for seeing and stopping these threats before they can cause damage.
The text describes “Living off the Land” attacks using trusted tools like PowerShell. How does AI-powered behavior analysis distinguish malicious activity from legitimate admin tasks? Could you walk us through a specific example of what that detection process looks like for a SOC analyst?
That’s the core of the problem, isn’t it? For years, security was about finding the bad file, the malware. But with “Living off the Land,” there is no bad file. AI-powered behavior analysis shifts the focus from the tool to the intent. It’s not about seeing that PowerShell is running; it’s about understanding the story of why it’s running. For a SOC analyst, this is a game-changer. Instead of a meaningless alert that a script ran, they see a full narrative: a user opened an email, a macro executed a PowerShell command which then attempted to connect to an unusual external IP and modify a registry key. The AI connects these dots in real-time, recognizing this sequence is completely abnormal for that user and flagging it as a high-fidelity threat, cutting through the noise of legitimate admin work.
You mention fileless “Last Mile” attacks using obfuscated HTML where no payload hits the endpoint. Since there’s no file to scan, what specific behaviors does a zero-trust model analyze to detect this threat? Please share a step-by-step example of how this is surfaced.
This is where legacy tools just go blind. They are waiting for a payload to land so they can scan it, but it never does. A zero-trust model operates on the principle of “never trust, always verify,” which means it scrutinizes the process, not the file. When a user browses to a site, the cloud-native inspection sees the obfuscated HTML and JavaScript. Instead of letting it run unchecked, the system analyzes its behavior in a secure environment. It sees the script attempting to reassemble malicious logic in the browser’s memory, making system calls that a normal webpage has no business making, or trying to establish a covert channel back to an attacker. This chain of suspicious behavior is what triggers the block—the attack is neutralized before that final, malicious instruction ever executes on the user’s actual machine.
The webinar notes that securing CI/CD pipelines is a major challenge. Given the heavy reliance on encrypted traffic and third-party repositories, how does cloud-native inspection provide visibility without slowing developers? What key metrics should security architects track to measure effectiveness in these environments?
Developer environments are the new frontier for attackers because they are built for speed and agility, not for traditional security gates. You can’t just throw a legacy firewall in front of a CI/CD pipeline; you’d bring the entire development process to a grinding halt, and the developers would rightly revolt. Cloud-native inspection provides visibility by being built into the fabric of the cloud itself. It can inspect all encrypted traffic seamlessly without requiring developers to change their workflows or tools. For security architects, the key metrics aren’t about how many threats you block, but how you enable the business. They should track the reduction of malicious code or risky dependencies identified pre-deployment and the mean time to remediate those findings. The ultimate goal is a frictionless process where security is baked in, not bolted on.
Your webinar positions AI-powered zero trust as the solution for these “hidden-in-plain-sight” attacks. Beyond just detection, how does this model proactively prevent attacks from ever reaching production systems? Could you detail the specific role that behavior analysis plays in that preventative step?
Detection is great, but prevention is the real goal. You don’t want to be told you were just breached; you want to stop the breach from ever happening. AI-powered behavior analysis is the engine that drives this prevention. It learns the normal rhythm of your environment—what applications talk to each other, what processes a user typically runs, what a server’s traffic patterns look like. The zero-trust model uses this baseline to create and enforce policy. When an attacker tries to use a tool like WMI to move laterally to another server, the system doesn’t just see WMI running. It sees a process attempting an action that is a stark deviation from its established, trusted behavior. The model then proactively severs that connection before it can succeed, preventing the attack from ever gaining a foothold in production. It’s not a postmortem alert; it’s a closed door.
What is your forecast for the evolution of “Living off the Land” and fileless attacks, and what single change will be most critical for security teams to adapt?
My forecast is that these attacks will simply become the norm. The days of easily spotting a malicious executable are fading fast. Attackers will get even better at blending into the background noise of a busy network, using an organization’s own tools and processes against them. The single most critical change for security teams must be a fundamental shift in perspective. They have to move away from an “indicator-based” mindset, where they hunt for known-bad files or signatures, to a “behavior-based” one. The defining question can no longer be “Is this file malicious?” It must become “Is this activity, in this context, normal?” Making that transition—in technology, process, and training—will be the difference between staying ahead of the threat and constantly cleaning up after it.
