Spectorn AI Security for Telecom Operators

Note

Industry specifics Telecom operators manage millions of subscriber sessions simultaneously, so latency budgets matter. Spectorn is designed for low-latency inline gateway checks, but production latency must be measured on the target deployment.

Current status: This page describes the threat model, implemented core primitives, and a target integration profile. Spectorn sees only explicitly submitted events; the external system enforces blocking. Structured temporal_safety is implemented in the single-tenant core API, while multi-tenant /v1/observe remains fail-closed until tenant isolation and fresh cross-tenant tests are complete.

Telecom infrastructure is going through a wave of AI integrations: from AI assistants in call centers to autonomous agents that manage the network (Network Operation Center — NOC). Every one of these agents works with sensitive subscriber data and has access to critical network commands.

Key threats and Spectorn defenses

1. Injection into NOC agents (Network Manipulation)

Attack scenario: An attacker sends a specially crafted request into the operator's network, knowing that an AI-NOC agent is on the other end. An injection inside the request body coerces the agent into running a shutdown_interface command or altering routing.

Spectorn defense:

The injection and goal_predictability engines inspect every agent command before it executes, heuristically flagging attempts to derail the agent from its intended goal.
Structured temporal_safety (TSA) can check an action sequence through a tenant-owned core registry; the hosted multi-tenant intake remains unavailable. This is monitoring, not a formal proof.

Caution

Critical infrastructure An attack on a telecom operator's NOC agent via prompt injection is an attack on critical infrastructure. In a target integration, goal_predictability supplies a heuristic signal and temporal_safety a sequence signal; the orchestrator must enforce both before a network command.

2. Subscriber data leakage (Subscriber PII Exfiltration)

Scenario: A call-center AI agent has access to a CRM containing subscriber data. A fraudster calls and, through multi-step manipulation, coerces the bot into disclosing the IMSI, address, or call records of someone else's number.

Spectorn defense:

The pii engine plus cross-tenant SOC isolation (partitioning events by tenant_id) help enforce that the agent only returns data for the authorized subscriber.
Every CRM query is recorded in the Decision Logger with a cryptographic hash.

YAML

# spectorn.yaml — telecom profile
engines:
  pii:
    action: block
    patterns:
      - MSISDN        # Subscriber number
      - IMSI          # SIM identifier
      - subscriber_address
  injection:
    action: block
    confidence_threshold: 0.75
  # Cross-tenant isolation is enforced by the SOC architecture (partitioning
  # events by tenant_id), not by a separate engine.

temporal_safety:
  enabled: true                                       # target profile; hosted /v1/observe is currently closed

3. Internal data leakage through a corporate AI service

Scenario: An employee routes internal network logs or CRM exports through a corporate AI service (a NOC assistant, a support chatbot), and the request or the model's response ends up carrying confidential subscriber data and fragments of internal configuration.

Spectorn defense:

Spectorn deploys inline in front of the corporate AI service as a gateway/proxy and inspects the content of every request and model response — this is content inspection at the gateway, not a network egress filter.
The pii and exfiltration engines recognize subscriber personal data and anomalous bulk exports in the inbound content; output_scanner inspects the model's response and blocks or redacts policy-violating content before it returns to the user.

Tip

Zero Cloud Dependency Spectorn runs entirely offline inside the operator's isolated network. No data leaves the perimeter for cloud-based analysis — the Spectorn platform itself deploys inside the perimeter.

Telecom-specific correlation rules

JSON

{
  "name": "NOC_COMMAND_INJECTION",
  "description": "Prompt injection preceding a network command from the agent",
  "condition": "sequence(injection_detected[confidence>0.65], agent_tool_call[category='network_command'], 15s)",
  "severity": "CRITICAL",
  "playbook": "suspend_noc_session_and_alert_security"
}

Compliance: SORM and 374-FZ

Spectorn does not intercept or store the content of subscriber communications — only metadata about AI interactions.
Decision Logger audit logs export to SIEM-compatible JSON. Compatibility with SORM-3 formats (SORM is Russia's lawful-interception system) has not been verified.

❓ FAQ

How does Spectorn stop prompt injection from hijacking a NOC network agent?

An integration can submit commands to injection and goal_predictability, and sequences to core temporal_safety; its external orchestrator must enforce the verdict before shutdown_interface or traffic rerouting. Hosted multi-tenant /v1/observe remains unavailable.

Can a telecom AI agent be tricked into leaking another subscriber's IMSI or call records?

Spectorn lowers this risk; it does not rule it out. The pii engine combined with cross-tenant SOC isolation (events partitioned by tenant_id) helps keep a call-center agent from returning another subscriber's data, and every CRM query is logged with a cryptographic hash, so multi-step social-engineering attempts leave a trace.

How does Spectorn protect data passing through a corporate AI service?

Spectorn deploys inline in front of the AI service as a gateway/proxy and inspects the content of requests and responses: the pii and exfiltration engines recognize subscriber personal data and anomalous bulk exports in the inbound content, while output_scanner blocks or redacts a policy-violating response before it returns to the user. This is content inspection at the gateway, not a network egress filter — Spectorn does not intercept arbitrary outbound HTTPS traffic and does not block calls to external providers at the network perimeter.

Is Spectorn compatible with SORM and 374-FZ requirements?

SORM and 374-FZ compliance has not been verified, and Spectorn is not a SORM system. What is known: Spectorn does not intercept or store the content of subscriber communications, only AI-interaction metadata, and its Decision Logger exports SIEM-compatible JSON. The carrier assesses its own system's compliance together with counsel.

What latency does Spectorn add at telecom scale?

Spectorn is designed for high-throughput gateway deployments. The added latency depends on deployment topology, scan policy, traffic shape, and provider routing, so telecom deployments should validate p95/p99 latency during rollout instead of relying on a generic microbenchmark claim.