Different sectors. Different regulators.
Same runtime gap.
Smart cities, telecom carriers, energy operators, oil & gas, transport & logistics, eCommerce, gaming and GPU clouds all live with very different threat models, regulatory frames, and connectivity profiles. What they share is a brittle runtime layer — and a regulator (NIS2, CRA, ISO/IEC 62443, HIPAA, PCI DSS) that no longer accepts "we believe we're secure." Each industry below is mapped to the AI EdgeLabs configuration that fits.
Pick your sector. See your fit.
Each industry card maps the most common threats, the regulators in scope, the AI EdgeLabs configuration, and the relevant case study where one exists.
Smart Cities
Connected infrastructure exposes operational technology to attackers. NIS2 and EU CRA now mandate built-in intrusion detection and response — runtime protection has moved from optional to essential.
Most common threats
- IoT-to-edge attack paths — compromised sensors pivoting into edge gateways or SCADA
- Ransomware against transit, utilities, emergency response
- DDoS that cripples city services and trust
- Zero-day exploits in OT & ML toolchains
How AI EdgeLabs fits
- Lightweight runtime protection on edge devices & orchestrators
- Unified visibility from traffic lights to city-wide datacentres
- Autonomous response with AI-generated playbooks
- Virtual patching for unpatched OT & ML components
Telco / 5G
5G, virtualization and MEC create a perfect storm of complexity and exposure. NIS2, CRA, and ISO 27001 now require continuous intrusion detection, vulnerability management, and rapid incident response across thousands of base stations, edge nodes, and cloud cores.
Most common threats
- Distributed multi-layer network attack surface
- APTs exploiting 5G supply chain & core-to-edge interfaces
- DDoS & service-disruption attacks against SLAs
- Subscriber data breaches & espionage
How AI EdgeLabs fits
- NDR + EDR + IDS/IPS in one inline AI-native agent
- Containerized, multi-tenant — uCPE to carrier core
- Inline AI detection at 50+ Gbps with < 2% CPU overhead
- 3–5× lower TCO vs legacy EDR/XDR stacks
Energy
The energy sector is the fourth-most-attacked industry, accounting for 10.7% of all cyber incidents. The average breach costs $4.72M and a single short-term outage can cascade catastrophically across the grid.
Most common threats
- Nation-state & geopolitically-motivated attacks on grid assets
- Highly distributed infrastructure with cascading failure risk
- Bandwidth-constrained, intermittently connected sites
- Data sovereignty & residency obligations
How AI EdgeLabs fits
- Threat mitigation in air-gapped & intermittent environments
- Scales to thousands of global nodes in a single business day
- Sensitive data never leaves the site — compliance-by-design
- Edge processing eliminates analytical latency
Oil & Gas
High-value, mission-critical infrastructure increasingly targeted by sophisticated cyber threats. Disruption produces costly downtime, safety risks, regulatory penalties, and reputational damage — robust real-time protection is essential.
Most common threats
- Operational technology vulnerabilities — legacy control systems
- Ransomware against production systems & pipelines
- Insider threats with privileged access
- Supply-chain compromise via third-party vendors
How AI EdgeLabs fits
- NDR + EDR + IDS/IPS unified for holistic protection
- AI-driven playbook generation for rapid incident response
- Adaptable to bandwidth-constrained & remote sites
- 30% efficiency improvement in real customer deployments
Transportation & Logistics
Smart fleets, connected cargo, autonomous vehicles, and logistics hubs run on distributed intelligence. Each moving asset is operational value — and a potential cyber-entry point. NIS2, CRA, ISO 27001, and GDPR all apply.
Most common threats
- Converged IT & OT exposure — SCADA, telematics, WMS
- Ransomware against logistics platforms & fleet APIs
- Theft of cargo data, GPS telemetry, customer records
- Vehicles & sites with limited connectivity
How AI EdgeLabs fits
- Lightweight inline AI agent at the edge — even offline
- Single runtime view across vehicles, terminals, datacentres
- Virtual patching & compliance automation
- < 2% CPU overhead — fits bandwidth-limited fleets
eCommerce / Retail
Microservice migrations on Google Cloud, AWS, or Azure expose Kubernetes attack surface that point-in-time scanners miss. Customer trust depends on real-time intrusion detection across every transaction path.
Most common threats
- Container escape & lateral movement in microservices
- Credential stuffing & account takeover at AI scale
- API abuse against checkout & payment paths
- Supply-chain attacks via third-party packages
How AI EdgeLabs fits
- Real-time intrusion detection across microservices
- Faster incident response without slowing operations
- Stronger protection for sensitive transactions
- PCI evidence collected continuously, not periodically
Gaming
Cloud-based auction platforms, account systems, and player-data stores attract DDoS floods, cheat-tool authors, and well-funded data brokers. Player experience and revenue depend on millisecond-grade defence.
Most common threats
- DDoS floods against matchmaking & auction infra
- Player data breaches & account takeover
- Container abuse on Linux / Docker / K8s game backends
- Fraud against in-game economy
How AI EdgeLabs fits
- Real-time, autonomous protection across Linux / Docker / K8s
- Cuts incidents and safeguards player & revenue data
- Inline mitigation of DDoS and bot traffic
- Built for the millions-of-players scale of modern titles
Cloud & GPUaaS
Multi-tenant GPU clouds run extremely valuable hardware against the most aggressive threat landscape: APTs targeting AI training data, cryptojacking aimed at GPU cycles, and tenant-breakout aimed at IP theft.
Most common threats
- APTs against AI/ML training pipelines & weights
- Cryptojacking on idle GPU cycles
- Tenant-breakout in shared K8s / Linux multi-tenancy
- Data breach of customer model artefacts
How AI EdgeLabs fits
- Runtime model integrity + agent-level visibility
- Multi-layer protection for K8s + Linux GPUaaS
- Negligible overhead — preserves AI throughput
- Audit-ready evidence per tenant
Infrastructure / HCI
Hyperconverged platforms span edge, cloud, and datacentre — and absorb whatever the underlying customer threat model is. Protection has to be lightweight enough to ship in the platform itself.
Most common threats
- Ransomware spreading across edge + cloud + DC tiers
- DDoS & insider threats against management plane
- Performance-sensitive workloads that can't tolerate heavy agents
How AI EdgeLabs fits
- Lightweight, autonomous protection bundled in HCI
- Reliable, scalable platforms shipped to customers
- Security without sacrificing performance
Why the same agent fits all of them.
"Modern cyber incidents rarely remain confined to a single organization. They propagate through suppliers, service providers, and shared digital infrastructure — often crossing borders and sectors within hours." Defending across that spectrum requires a single agent that is small enough to fit a roadside camera and capable enough to defend a 5G core.
Distributed by default
Smart cities, telcos, energy, transport — all run on hundreds to thousands of nodes that cannot phone home for security decisions. The agent runs locally, on-host, with zero data egress.
Regulator-graded
Whatever the sector, NIS2, CRA, ISO/IEC 62443, HIPAA, PCI DSS, FedRAMP, or NIST is in scope. The Compliance Center keeps a single posture model and exports framework-specific evidence on demand.
Real-time or it doesn't count
Operational continuity, SLA targets, customer trust, public safety — none of them survive a multi-hour MTTR. Sub-millisecond inline detection and response is the same in every vertical.
Don't see your industry yet?
If your environment is regulated, distributed, or runs AI workloads — the pattern fits. Tell us about your sector and we'll map the closest reference deployment.