Implementing runtime security for the cloud [Q&A]

Cloud-native platforms are built for speed with ephemeral workloads, rapid deployments, and plenty of third-party app dependencies.

This poses a real challenge to the deployment of runtime security tools. We talked to Bob Tinker, founder and CEO of BlueRock.io, to discuss how organizations can protect their cloud systems effectively.

BN: Why is it particularly challenging to implement effective runtime security solutions in modern cloud environments?

BT: The sheer scale makes things exponentially harder. Organizations are now managing thousands of microservices that spin up and down in minutes, each with its own attack surface.

Many solutions still rely on privileged agents that slow workflows, require deep CI/CD integration, or even introduce new security liabilities. These legacy tools simply can't keep pace with containers that exist for minutes or hours, not months.

Meanwhile, the attack landscape has shifted dramatically. Exploits are weaponized at AI-speed, often within hours of CVE publication. Bad actors are leveraging automation and machine learning to identify vulnerable targets faster than human security teams can respond. By the time security tools detect an issue, it's too late. The window between vulnerability disclosure and active exploitation has shrunk from weeks to hours, completely changing the security game.

Today's imperative: runtime protection that operates without disrupting development velocity or app performance. Organizations need solutions that can secure running applications at runtime, not just static source code.

BN: What are the critical capabilities organizations should prioritize when evaluating runtime security solutions for cloud-native applications?

BT: Layered protection is key: detection and response helps and is where most security teams start. But detection isn't enough. Detection-only strategies leave organizations playing defense against attackers who have already gained a foothold. Now that threats emerge at AI-speed, prevention becomes essential.

To deal with AI-speed attacks we have to building systems that prevent, not just detect exploit behavior, including:

• Remote code execution, deserialization attacks, privilege escalation, and data exfiltration. These attack vectors represent the most common paths to compromise in cloud-native environments.
• Policy enforcement spanning app, container, and node runtime contexts. Security boundaries must be consistent across the entire stack, from individual micro-services to cluster-wide policies to specific cloud instances.
• Low friction deployment that is transparent to developers and DevOps CI/CD workflows and which doesn't slow down app performance. Any solution that requires developers to change their workflow or creates slow or unpredictable performance overhead will face adoption resistance and ultimately fail.

Detection lets you see. Prevention keeps you safe. The goal is to create a ‘secure-by-default’ runtime environment where attacks fail before they can cause damage.

BN: In the age of AI-speed attacks, is relying on traditional code scanning and analysis effective to assess risk from vulnerabilities?

BT: Static scanners serve a baseline, listing all of what might be vulnerable. But they can't tell you what's actually running in production. They operate with the assumption that every line of code is equally important, which simply isn't reality. That creates massive noise for developers: vulnerabilities flagged in dead code, unused modules, or unreachable paths. The result is “findings fatigue” that make organizations less secure as critical issues get lost in the noise, and reduces the time developers can speed on value-add innovation.

In the era of AI-speed attacks, we have to do a much better job of prioritization. Runtime context fills that gap. It answers: "Does this code run in production? Is it reachable? Is the vulnerable code-path invoked?" That transforms developer patch prioritization from theoretical to actionable, speeding the most important fixes and giving developers more time.

Simply put: Static analysis is tactical and overwhelms developers. Runtime reachability is accurate and saves developers enormous amounts of time to focus on what's most important. It's the difference between fixing everything and fixing what matters.

BN: To what degree can runtime context reduce scan/patch fatigue and improve prioritization in vulnerability management?

BT: It's a dramatic shift. Without having the vantage point of the application runtime, individual developers can end up wading through 30 to 50 flagged vulnerabilities every sprint, many or most of which are actually unexploitable. This creates a vicious cycle where security becomes synonymous with productivity drag, leading to shortcuts and workarounds that actually increase risk.

Add runtime insight, and that load collapses. In production environments, only a handful of vulnerabilities actually execute. The signal-to-noise ratio improves by an order of magnitude. Developers can focus on remediating three to five real threats rather than reopening hundreds of tickets. This focused strategy not only improves security outcomes but also rebuilds trust between security and development teams.

Mix reachability with runtime enforcement, and disruption shrinks even further. Development teams spend less time patching and more time delivering value. When prevention mechanisms can automatically block exploit attempts, developers get breathing room to address vulnerabilities during normal development cycles rather than emergency patches.

BN: How are MCP and agentic AI workflows creating new security concerns, and what are some of the most critical attack classes emerging in these environments?

BT: MCP and agentic AI introduces a new execution layer, bridging language models with live tools, infrastructure and data. It gives AI agents runtime privileges needed to invoke APIs, read/write sensitive data, and execute processes on critical systems. And yet the security model around it is immature.

Emerging attacks include:

• Prompt injection: manipulating model instructions. These attacks can be remarkably subtle, hidden in seemingly innocent data that the AI processes.
• Tool poisoning: subverting trusted workflows. Attackers can compromise the tools that AI agents rely on, turning trusted automation into attack vectors.
• Data exfiltration: tricking agents into accessing and siphoning information from internal systems. AI agents' ability to understand and process vast amounts of data makes them particularly effective at this type of attack.
• Credential theft: harvesting session secrets or API tokens from shared context. Agentic AI processes often have access to highly privileged credentials that traditional applications never required.
• Remote code execution: via exploit of systems on which AI agents execute. The platforms hosting these AI agents become high-value targets with broad access to organizational resources.

New approaches to security must emerge which protect both the runtime environments and interactions between agentic AI processes via MCP from malicious activity.

As an industry, we’re frankly not prepared to secure apps and data given the speed at which AI is changing the runtime context.

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