What’s the point of detecting an attack if you can’t stop its progress?  

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Many security teams today deal with this frustrating reality. Cloud attacks don’t wait around. They often move faster than the speed of human and technology-assisted response.

The old playbook of alerting, triaging, and manually responding to attacks simply can’t keep up anymore. On top of managing a sea of alerts, complex attack TTPs, and a range of fragmented security dashboards, security teams now need to beat threat actors at their own game. That’s why more security teams are considering automated response.

But here’s the challenge: automation depends on trust. And in the cloud, where noise is constant and false positives are everywhere, that trust is hard to earn. No one wants to isolate a workload or shut something down because of a harmless backup job. So security teams rule out automation as an option for threat mitigation, because they don’t have the right signals to make automated response reliable.

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Stream Traps flips that script.

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Cloud traps, deception assets embedded in cloud infrastructure, offer a proactive way to detect, delay, and divert attackers, buying security teams the signals and context they need to respond effectively.

Rather than chasing faster MTTR alone, cloud traps focus on slowing the adversary down — turning every interaction into a tactical advantage for defenders.

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Turning Back the Clock: Why On-Prem. Deception Didn’t Work

While the concept of deception isn’t new, legacy on-prem. deception tools failed to gain real traction due to several reasons:

High Operational Overhead

Traditional deception required manual configuration, dedicated hardware, and ongoing maintenance. Security teams had to constantly refresh decoys to avoid fingerprinting, creating an unsustainable burden.

Limited Scalability and Poor Placement

Deception assets had to be manually placed within specific network segments, often missing the locations attackers were likely to explore. As a result, decoys were rarely triggered and failed to deliver value.

Low-Fidelity Alerts

In noisy on-prem. environments, legitimate internal traffic—scripts, IT scans, automated jobs—frequently triggered deception alerts. The result was a stream of false positives that diluted trust and created alert fatigue.

The cloud changes these dynamics. When done right, cloud traps can be scalable, high-signal, and virtually maintenance-free.

Deception 2.0: Traps in the Cloud

Scalable by Design

Cloud infrastructure removes many of the barriers that made on-prem. deception difficult to operationalize. With infrastructure-as-code (IaC), traps can be deployed, rotated, an retired programmatically– no manual set up, no custom hardware, and no regular tuning required. This allows SecOps and DevOps teams to embed deception into workflows and implementation processes with minimal effort.  

Because assets are regularly spun up and down, introducing and refreshing traps becomes part of the normal rhythm of cloud operations—reducing friction and making deception viable at scale.

Native to the Cloud Threat Model

Adversaries in the cloud rely heavily on attack techniques that are built up by reconnaissance conducted in target environments. Threat actors commonly exploit identity roles and policies, misconfigurations, and overly permissive roles. These behaviors are not only common – they are also necessary steps in the attack chain.

This means that threat detection can be especially aligned with how attackers operate – and where defense teams choose to place traps. By using common cloud tactics, techniques, and procedures (TTPs) to track attacker behavior, security teams can place traps in highly visibility, high-probability locations. When traps are embedded in resources that are frequently targeted during reconnaissance or lateral movement, for example, attackers are more likely to engage—turning standard cloud behaviors into opportunities for early detection.

Increased Detection Fidelity

One of the most compelling benefits of cloud traps is the fidelity of the signals they produce.

Legitimate users and systems should never interact with deception assets. When someone engages with a trap, it’s an immediate indication of suspicious behavior. Unlike other detection methods that rely on behavioral baselines or anomalies, which can create noise and necessitate triage, traps offer rare clarity. Every trap engagement is a signal worth investigating –without the need to filter through false positives.

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Stream Traps: Real-Time Deception with Full Cloud Context

While cloud environments make deception more effective, successful deployment still depends on having the right visibility, context, and control.That’s where Stream Traps provides a differentiated approach.

The Stream Difference

The core of Stream’s operations is the CloudTwin™ - our real-time model of your entire cloud environment. The CloudTwin™ continuously reflects current configurations, identities, network relationships, and behavioral data. This real-time modeling enables traps to be deployed with ultimate precision:

• Traps can be placed near high-value assets and along realistic attack paths that are mapped based on real-time data.
• Naming conventions and policies are generated using LLMs that match your actual environment – ensuring decoys are indistinguishable from real assets.
• Trap rotation is automated, maintaining freshness and preventing threat actor detection.

Integration into the CDR Workflow

When a trap is triggered, Stream immediately analyzes the incident in context. The CloudTwin™ maps potential attack blast radius—showing what the attacker could have accessed or compromised had the engagement continued. WithStream, traps operate as a part of our detection capabilities, correlating fully with other detection signals that our platform collects. Security teams using Stream Traps are able to leverage strategic placement and signal detection as part of an end-to-end detection and response process.

High-Fidelity Alerts by Design

Stream adds an additional layer of control on top of standard cloud deception mechanisms to ensure trap signals are both high-confidence and highly actionable. While legitimate users may access cloud traps, they won’t move data out of the cloud environment. Stream Traps do not generate alerts on access alone—instead, they only trigger when a data exfiltration attempt occurs within the trap. This deliberate threshold ensures that alerts represent true adversary behavior, not internal missteps or benign scanning.

These controls effectively eliminate false positives. Security teams can treat every trap signal as a verified indicator of compromise, removing the need for triage and reducing alert fatigue.

Sandbox Isolation for Intelligence Gathering and Attack Delay

When an attacker engages with a Stream Trap, particularly through an assume-role or privilege escalation action, they can be redirected to a controlled sandbox environment. This sandbox is hosted in a separate, secure cloud account managed by Stream and designed to replicate the target environment.

In this space, the attacker believes they’re progressing through the environment, but all activity is isolated and monitored. This allows Stream to collect real-time telemetry on attacker behavior, tools, and intent without risk to production systems. The result is both containment and intelligence: defenders gain insight into TTPs while attackers are delayed and diverted away from real assets.

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On-prem. deception fell short because it was heavy, noisy, and hard to maintain. The cloud flips that model on its head. With the right visibility and context, traps become a lightweight, high-signal addition to any detection strategy.

Stream Traps, powered by the CloudTwin™, delivers scalable, cloud context-aware deception that integrates seamlessly into your CDR workflow—no manual upkeep, no alert fatigue, just actionable signals when they matter most.

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Ready to see how cloud deception actually works? Book a demo with our team and watch Stream Traps in action.

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