AI Spots the Quiet Attacks Hiding on Your Network Edge

Most security teams still picture critical infrastructure attacks as loud: a zero-day, a ransomware detonation, a panicked war room. Amazon’s recent disclosure of a years-long GRU-linked campaign targeting energy and cloud-hosted infrastructure shows the opposite pattern wins more often—quiet persistence, boring misconfigurations, and credential theft that blends into normal traffic.

What’s unsettling isn’t just that the activity ran from 2021 to 2025. It’s how practical it was: compromise misconfigured network edge devices, turn on packet capture, harvest credentials, then try credential replay against downstream services. No Hollywood malware required.

For this AI in Defense & National Security series, this story is a clean example of why AI in cybersecurity matters: multi-year campaigns don’t fail because defenders lack tools—they fail because defenders can’t connect faint signals across time, tenants, and telemetry sources fast enough. Machine learning-based detection (done right) is built for exactly that.

What Amazon’s GRU finding really tells defenders

Answer first: The GRU campaign highlights a shift from “break in with exploits” to “log in with stolen credentials” by owning the network edge—especially cloud-hosted edge appliances.

Amazon attributed the activity with high confidence to Russia’s GRU, citing infrastructure overlaps with a cluster commonly associated with APT44 (aka Sandworm / Seashell Blizzard). The important operational detail is the tactical adaptation: Amazon observed that N-day and zero-day exploitation declined over time, while targeting misconfigured edge management interfaces stayed consistent.

This is the part many orgs miss: patching matters, but attackers will gladly take the cheaper path.

The edge-device “credential vacuum” pattern

Answer first: If an adversary controls your edge appliance, they can often see credentials before your identity stack ever has a chance.

Amazon described a playbook that’s painfully efficient:

1. Compromise a customer network edge device hosted on cloud infrastructure
2. Use native packet capture capability
3. Pull credentials from intercepted traffic
4. Attempt credential replay against online services
5. Establish persistence and move laterally

Think about what that means operationally. Even strong IAM programs can be undermined if credentials (or session artifacts) are exposed at the edge. In energy and telecom supply chains—where third parties, MSPs, and integrators touch sensitive environments—this is a gift to an espionage-focused actor.

Why this matters for defense and national security

Answer first: Cloud-hosted routing, VPN, and management planes are now part of national security surface area.

Energy operators, regional utilities, telecoms, and the cloud providers that host their tooling are tightly coupled. That coupling creates a strategic opportunity: compromise the supply chain adjacency (a hosted edge appliance or management platform), then pivot toward higher-value targets.

This is exactly the kind of activity that doesn’t always trigger traditional “incident” thresholds, but it absolutely creates strategic risk.

Why long-running campaigns are an AI detection problem

Answer first: Multi-year intrusion sets are hard to stop because the signals are weak individually, but obvious when correlated—AI is built for correlation at scale.

Traditional detection workflows are still optimized for discrete events:

• a known-bad hash
• a high-severity CVE exploit attempt
• a ransomware note

But Amazon’s write-up points to a different reality: persistent connections to compromised instances running network appliance software, interactive access patterns, and credential replay attempts that may fail but still map the attacker’s intent.

Those signals are not always high-confidence alerts. They’re patterns.

What AI catches that humans and rules miss

Answer first: AI helps when you need to notice “slightly weird” behavior across weeks or months, not “obviously malicious” behavior in five minutes.

A few examples where AI-driven cloud defense tends to outperform static rules:

• Behavioral baselining for edge appliances: If an EC2 instance that usually talks to a small set of IPs starts maintaining long-lived interactive sessions to new infrastructure, anomaly detection can flag it even if the IPs aren’t on threat lists.
• Cross-asset correlation: A single failed credential replay attempt is noise. A cluster of them, tied to a new packet-capture process on an edge appliance, becomes a story.
• Time-series drift detection: Campaigns that “slow roll” changes (new admin login patterns, gradual expansion of IP ranges, repeated access at off-hours) are designed to look normal. Models trained on seasonality can surface that drift.

I’ve found the best results come when AI isn’t treated as a magic alert generator, but as a signal-ranking engine: it tells analysts which 2% of weirdness deserves the 80% of human attention.

The myth: “If we patch fast, we’re fine”

Answer first: Patching closes doors, but misconfiguration and identity exposure are still wide-open windows.

Amazon’s timeline included vulnerability exploitation (e.g., WatchGuard Firebox/XTM, Confluence flaws, a Veeam issue), but the consistent theme was misconfigured edge devices with exposed management interfaces.

That’s an uncomfortable truth for many orgs: you can hit patch SLAs and still lose if your edge fleet has:

• exposed admin panels
• weak or reused credentials
• over-permissive security groups
• no egress control
• logging gaps

AI can help here too—by continuously evaluating configuration drift and prioritizing fixes based on observed attacker behavior.

How the attack path maps to cloud and critical infrastructure reality

Answer first: The campaign worked because edge devices sit at the intersection of cloud, identity, and operational access.

Amazon noted targeting across:

• enterprise routers and routing infrastructure
• VPN concentrators and remote access gateways
• network management appliances
• collaboration/wiki platforms
• cloud-based project management systems

This mix is telling. Espionage actors don’t just want a shell; they want the documentation and workflows that explain how environments are run—network diagrams, runbooks, credentials in wikis, tickets that reveal what changed and why.

Why packet capture is such a powerful (and overlooked) abuse case

Answer first: Packet capture is a legitimate feature that becomes a credential-harvesting weapon when the attacker is on the edge.

Edge appliances often support packet capture for troubleshooting. If an attacker gains admin control, they can capture:

• authentication flows to internal services
• API tokens moving between systems
• legacy protocols where credentials travel unsafely
• session metadata that helps plan follow-on access

This is why “secure the identity provider” isn’t enough. You also have to secure the places credentials transit.

Supply chain targeting isn’t a buzzword here

Answer first: The energy sector supply chain is targeted because it offers access paths that are cheaper than hitting operators directly.

Amazon described targeting that includes both direct operators and third-party providers. That lines up with how critical infrastructure is actually run: vendors, contractors, managed service providers, and cloud-hosted tooling create many legitimate pathways—perfect for credential replay.

From a defense-and-national-security perspective, the practical question becomes: Which third parties can reach operationally sensitive networks, and how is that access monitored?

A practical playbook: AI-driven defenses that map to this campaign

Answer first: The fastest wins come from combining hard controls at the edge with AI-based detection that watches for persistence, capture behavior, and replay patterns.

Below is a concrete, campaign-aligned checklist security teams can execute without waiting for a full platform overhaul.

1) Lock down edge management like it’s production identity

Answer first: Most compromises start at exposed management. Remove exposure and the attacker’s cheapest option disappears.

• Eliminate public management interfaces wherever possible (move to private access paths)
• Enforce phishing-resistant MFA for admin access (not SMS)
• Require device-unique credentials; kill shared admin passwords
• Use just-in-time admin elevation with short session lifetimes

2) Use AI to detect “edge-device acting like a workstation”

Answer first: Edge appliances should be predictable. AI can flag when they start behaving like interactive hosts.

What to model and alert on:

• new long-lived outbound sessions from edge instances
• unusual interactive protocols from network appliances
• increases in data retrieval patterns inconsistent with routing/VPN function
• rare processes like packet capture started outside approved maintenance windows

3) Treat credential replay as a high-signal symptom, not noise

Answer first: Failed replay attempts still prove credentials were harvested and tested.

Operationalize this:

• build detections for repeated auth attempts against the same service from new geographies
• correlate replay attempts with recent edge configuration changes or new admin sessions
• score anomalies higher when they involve privileged apps (cloud consoles, project systems, wiki platforms)

4) Instrument the “cloud edge” the same way you instrument endpoints

Answer first: If an EC2 instance runs routing/VPN software, it deserves EDR-like visibility.

Minimum telemetry:

• network flow logs (ingress/egress) with retention long enough to catch slow campaigns
• instance-level process and command execution logs where feasible
• configuration change audit trails (security groups, IAM roles, appliance configs)

AI-based analytics become dramatically more accurate when the data is consistent and retained.

5) Make misconfiguration drift measurable

Answer first: Attackers love drift because it creates accidental exposure. AI can prioritize which drift actually matters.

• continuously scan for exposed management ports and newly permissive rules
• rank findings by proximity to critical services and observed attacker TTPs
• auto-open tickets with owner, impact, and fix steps (fast routing beats perfect reporting)

“People also ask” questions security leaders are asking this week

Is this mainly a cloud security issue or a network security issue?

Answer first: It’s both—because cloud-hosted edge devices collapse network and cloud boundaries.

If your router/VPN concentrator is an instance in the cloud, you need cloud controls (IAM, security groups, audit trails) and classic network controls (segmentation, edge hardening) working together.

Does AI replace threat intel and SOC analysts?

Answer first: No—AI makes analysts faster by turning scattered weak signals into prioritized investigations.

Threat intel tells you what to look for. Analysts decide what it means in your environment. AI helps with the part humans are worst at: correlation at scale over long periods.

What’s the single most actionable control from this campaign?

Answer first: Remove or tightly restrict exposed management interfaces on edge devices.

It’s not glamorous, but it breaks the cheapest initial access vector described across multiple years.

Where this goes next for AI in defense & national security

The strategic shift is clear: state-backed operators are investing in repeatable access—misconfiguration abuse, credential harvesting, and supply chain adjacency—because it scales across regions and sectors.

If you’re responsible for energy, telecom, government, or any supplier that touches them, AI in cybersecurity isn’t optional window dressing. It’s how you detect slow intrusion sets that are designed to sit below the alert threshold.

If your team wants a practical next step, start by mapping your edge appliance fleet (including cloud-hosted routing and VPN instances), then define what “normal” looks like for their network behavior. Once you can measure normal, AI can spot the quiet deviations that matter.

What would your incident response look like if you discovered this kind of access had been quietly collecting credentials for 18 months—and which signals, today, would tell you that’s already happening?