SYNTREX AI Security for Telecom Operators
Industry specifics Telecom operators manage millions of subscriber sessions simultaneously. Even 10 ms of latency is unacceptable. That is precisely why SYNTREX Shield is written in C and Rust — the added latency is under 100 microseconds.
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 SYNTREX 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.
SYNTREX defense:
- The
injectionandgoal_predictabilityengines inspect every agent command before it executes, heuristically flagging attempts to derail the agent from its intended goal. - The
temporal_safetymonitor (TSA) checks the agent's action sequence against a runtime safety automaton — on-the-fly monitoring, not a formal proof.
Critical infrastructure
An attack on a telecom operator's NOC agent via prompt injection is an attack on critical infrastructure. SYNTREX combines heuristic goal-hijack detection (goal_predictability) with runtime temporal-safety monitoring (temporal_safety/TSA) to protect agent network commands.
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.
SYNTREX defense:
- The
piiengine plus cross-tenant SOC isolation (partitioning events bytenant_id) guarantee that the agent only returns data for the authorized subscriber. - Every CRM query is recorded in the Decision Logger with a cryptographic hash.
# syntrex.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 # runtime monitoring of the agent's action sequence
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.
SYNTREX defense:
- SYNTREX 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
piiandexfiltrationengines recognize subscriber personal data and anomalous bulk exports in the inbound content;output_scannerinspects the model's response and blocks or redacts policy-violating content before it returns to the user.
Zero Cloud Dependency SYNTREX runs entirely offline inside the operator's isolated network. No data leaves the perimeter for cloud-based analysis — the SYNTREX platform itself deploys inside the perimeter.
Telecom-specific correlation rules
{
"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
- SYNTREX does not intercept or store the content of subscriber communications — only metadata about AI interactions.
- Decision Logger audit logs are compatible with the SORM-3 reporting format (SORM is Russia's lawful-interception system), exporting to SIEM-compatible JSON.
❓ FAQ
How does SYNTREX stop prompt injection from hijacking a NOC network agent?
The injection and goal_predictability engines inspect every agent command before it runs, and the temporal_safety monitor (TSA) checks the action sequence against a runtime safety automaton. This blocks crafted requests that try to trigger shutdown_interface or reroute traffic.
Can a telecom AI agent be tricked into leaking another subscriber's IMSI or call records?
Not with SYNTREX. The pii engine combined with cross-tenant SOC isolation (events partitioned by tenant_id) ensures a call-center agent only returns data for the authorized subscriber, and every CRM query is logged with a cryptographic hash, defeating multi-step social-engineering attempts.
How does SYNTREX protect data passing through a corporate AI service?
SYNTREX 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 — SYNTREX does not intercept arbitrary outbound HTTPS traffic and does not block calls to external providers at the network perimeter.
Is SYNTREX compatible with SORM and 374-FZ requirements?
Yes. SYNTREX never intercepts or stores the content of subscriber communications — only AI-interaction metadata. Its Decision Logger exports SIEM-compatible JSON aligned with the SORM-3 reporting format (SORM is Russia's lawful-interception framework) and 374-FZ.
What latency does SYNTREX add at telecom scale?
SYNTREX Shield is written in C and Rust specifically for high-throughput environments, adding under 100 microseconds of latency — well within the sub-10 ms budget required to handle millions of concurrent subscriber sessions.