Your AI is Live.
Now Make it Secure.
Southeast Asian enterprises are deploying AI fast — and regional regulators are responding. SentraSuite gives your security team full control: from posture and red teaming to runtime enforcement, with built-in evidence for PDPA, MAS AI Principles, and the ASEAN AI Governance Framework.
AI adoption is accelerating. Security is lagging.
Your SIEM, WAF, and DLP were built before LLMs existed. They have no visibility into prompt injections, agentic pipelines, or MCP session exploits — the attack surfaces that matter most right now.
Runtime prompt attacks
Prompt injections, jailbreaks and data exfiltration attempts targeting AI assistants in production.
AI model vulnerabilities
New CVEs targeting LLMs, agent frameworks and MCP servers emerge every week.
Blind AI posture
No visibility into AI system configurations, data flows or compliance gaps across the enterprise.
Compliance gaps
PDPA, MAS and IMDA mandates increasingly cover AI governance — most organisations are not ready.
Four products. One unified AI security platform.
Each product targets a distinct layer of the AI attack surface — deploy individually or as an integrated suite.
SentraGuard
Real-time protection for every prompt, response and file interaction flowing through your AI systems.
- Prompt injection & jailbreak detection
- PII / sensitive data redaction
- Voice input & file upload blocking
- Agentic AI & MCP session control
- Inline enforcement on every AI interaction
AIThreatIntel
Continuous intelligence on AI-specific vulnerabilities, mapped to your deployed models and frameworks.
- Curated AI/ML CVE database
- Organisation-specific risk scoring
- MITRE ATLAS & OWASP LLM Top 10
- Early warning on emerging AI threats
- Analyst + agentic triage workflow
SentraScan
Automated discovery and risk assessment of all AI assets, configurations and data pipelines across the enterprise.
- AI asset inventory & classification
- Configuration & exposure scanning
- Supply chain & model integrity checks
- Compliance gap reporting (PDPA, MAS, ASEAN)
- Continuous or on-demand scanning
SentraRed
Automated adversarial testing of your AI models and applications before threats find them in production.
- Comprehensive adversarial prompt coverage
- Multi-turn attack chain simulation
- Agentic workflow penetration
- Risk-scored remediation reports
- Scheduled & CI/CD-integrated testing
Build-phase hardening that feeds directly into runtime protection.
Four products spanning the complete AI security lifecycle — build-phase hardening feeds directly into runtime enforcement.
The build-time security journey
SentraScan maps your attack surface. SentraRed probes every weakness. What they find becomes the policy SentraGuard enforces in production.
The SentraGuard journey — every AI interaction protected
Every prompt is inspected twice — before and after the model — with build-phase policies and live AIThreatIntel rules enforced at every interaction.
The AIThreatIntel operational journey
AIThreatIntel runs continuously — ingesting global AI threat signals, correlating them against your specific AI stack, scoring risk in your organisational context, and pushing live detection rules directly into SentraGuard.
Simplify your next AI audit with built-in control evidence.
SentraSuite is designed around Southeast Asian AI and data governance frameworks and global AI security standards — so your team spends less time assembling control evidence.
Technology is only half the answer. Governance makes it stick.
AI security tools without governance frameworks create a false sense of coverage. SOAISEC Labs closes the gap: strategic consultancy, structured risk assessment, and the SentraSuite tools to operationalise every policy you set.
Our services and tools map to every stage — from initial assessment through to continuous assurance.
Designed for regulated sectors across Southeast Asia.
Where AI adoption is highest and the consequences of a breach are most severe.
Banking & Finance
MAS, OJK and BSP compliance with AI runtime guardrails for customer-facing models
Government
Sovereign deployment options, IMDA and MAS-aligned controls, classification-aware AI policies
Energy & Utilities
Operational AI integrity across critical infrastructure with zero-trust enforcement
Healthcare
Patient data protection with PDPA-aligned AI redaction and audit trails
Telecom
Network AI security, model drift detection and subscriber data governance
The AI security platform built from the ground up for Southeast Asian compliance requirements.
Generic security tools were never designed for LLMs, agents, or agentic pipelines. SentraSuite was. Here's what that means for your organisation.
⚡ Operational in days, not months
Pre-built integrations for leading LLM platforms, agent frameworks and MCP servers. Go live without a 6-month professional services engagement.
📋 Built-in compliance evidence from day one
Stop assembling audit evidence by hand. PDPA, MAS, IMDA, and AI-specific framework mappings are pre-built — every interaction generates evidence automatically, cutting audit preparation from weeks to hours.
🔒 Designed for data sovereignty
With on-premise or sovereign cloud deployment, your data does not need to leave your environment — supporting the stringent data residency requirements of Southeast Asian regulators including MAS, IMDA, OJK and national DPAs.
⏱ Near-real-time inline enforcement
SentraGuard inspects every prompt and validates every response inline — with latency your users won’t notice and coverage your auditors will.
💰 Lower TCO than point solutions
A single platform covering posture, red teaming, runtime enforcement and threat intelligence replaces four separate vendor contracts — reducing integration overhead, vendor management cost, and licensing complexity.
🧩 Start modular, grow to a full suite
Deploy the product that solves your most urgent risk today. Add SentraSuite modules as your AI programme scales — with no rip-and-replace and a unified data model across the suite.
🤖 Ready for the agentic AI era
Purpose-built for autonomous agents, multi-model pipelines and MCP servers — the attack surfaces your existing SIEM and WAF tools cannot see.
Your AI security programme — by industry and timeline.
Select your industry for a tailored roadmap. Two parallel tracks — third-party AI your teams use today, and AI systems your organisation is building — each with clear milestones at 3, 6, 12 and 24 months.
- ›Inventory all sanctioned & shadow AI tools in use
- ›Classify data sensitivity flowing to each SaaS AI platform
- ›Draft Acceptable Use Policy for AI tools
- ›Block highest-risk unsanctioned AI access
- ›OWASP LLM Top 10 baseline risk assessment
- ›Deploy AI gateway proxy for approved AI tools
- ›PII & sensitive data redaction at prompt layer
- ›Immutable audit trail for all AI interactions
- ›User awareness & training programme
- ›Integrate with CASB / DLP for data egress monitoring
- ›Full PDPA / MAS AI compliance documentation
- ›AI usage governance dashboard live
- ›Third-party AI vendor risk assessments completed
- ›Automated policy enforcement across all tools
- ›AI incident response playbook for data leaks
- ›ISO/IEC 42001 certification readiness achieved
- ›Board-level AI governance reporting in place
- ›Predictive risk scoring for new AI tools
- ›Automated audit evidence generation
- ›AI usage ROI analytics for business stakeholders
- ›Threat model all planned AI systems & data pipelines
- ›Define AI security requirements & SAIDL process
- ›Training data governance & provenance controls
- ›OT: Network segmentation for AI in ICS/SCADA
- ›SentraScan initial AI asset baseline assessment
- ›SentraRed adversarial testing before go-live
- ›SentraGuard deployed inline for all GenAI APIs
- ›RAG pipeline security controls & data access policies
- ›Model supply chain & dependency verification
- ›OT: AI integrity monitoring for operational systems
- ›Quarterly red team & adversarial testing cadence
- ›AIThreatIntel live CVE feed integrated to all models
- ›AI-specific incident response playbook active
- ›OT: AI-specific ICS security controls & anomaly detection
- ›Compliance evidence auto-generated for auditors
- ›AI Centre of Excellence with embedded security
- ›Zero-trust AI architecture across all deployments
- ›Autonomous threat response & self-healing policies
- ›OT: AI-powered operational resilience & predictive threat
- ›NIST AI RMF + ISO/IEC 42001 external audit ready
- ›Inventory AI tools used in clinical & admin workflows
- ›Classify PHI / patient data flowing to each SaaS AI tool
- ›Draft Clinical AI Acceptable Use Policy (aligned to PDPA)
- ›Identify MAS AI-sensitive data paths to AI platforms
- ›Block unsanctioned clinical AI access to patient records
- ›Deploy AI gateway for approved clinical & admin AI tools
- ›Automatic PHI detection and blocking at prompt layer
- ›PDPA-compliant audit trail per patient interaction
- ›Clinical staff training on safe AI use (data protection)
- ›Integrate with hospital DLP for patient data egress control
- ›PDPA / MAS AI compliance documentation for AI tools
- ›Medical AI vendor assessments & data processing agreements
- ›Clinical AI governance committee established
- ›Automated policy enforcement across all clinical AI tools
- ›AI data breach response playbook (patient safety obligations)
- ›Automated PDPA evidence generation for healthcare AI
- ›Board-level clinical AI oversight reporting
- ›AI-assisted clinical documentation — zero PHI leakage controls
- ›Predictive risk scoring for new clinical AI tools onboarding
- ›ISO/IEC 42001 + JCI AI safety documentation ready
- ›Threat model medical AI systems (imaging, diagnostics)
- ›De-identification & anonymisation framework for training data
- ›Clinical AI security requirements aligned to MAS AI
- ›Define model explainability requirements for clinical use
- ›Data minimisation controls for inference on patient data
- ›SentraRed adversarial testing of diagnostic models pre-clinical deployment
- ›SentraGuard inline for all patient-facing AI APIs
- ›Model explainability & bias baseline established
- ›Role-based access controls for AI model inference
- ›Secure data pipeline for clinical model training & updates
- ›Quarterly red team on clinical AI models in production
- ›AIThreatIntel integrated for medical AI CVEs & MITRE ATLAS
- ›Clinical AI incident response playbook (patient safety implications)
- ›Regulatory evidence pack for MAS AI / PDPA audits
- ›Model drift monitoring with clinical alert thresholds
- ›Clinical AI Centre of Excellence with embedded security
- ›Zero-trust architecture for patient data AI systems
- ›Autonomous PHI protection across all AI interactions
- ›Predictive monitoring for clinical AI model drift
- ›ISO/IEC 42001 + healthcare AI safety documentation ready
- ›Inventory AI tools accessing MNPI & customer financial data
- ›Classify data sensitivity: MNPI, KYC, transaction & credit data
- ›Draft AUP aligned to MAS FEAT AI governance requirements
- ›Identify MAS/OJK / MAS/BSP-regulated data flowing to SaaS AI
- ›Block AI access to regulated trading & customer data without controls
- ›Deploy AI gateway for trading desk & analyst AI tools
- ›Automatic MNPI & customer data detection and blocking
- ›MAS FEAT-compliant immutable audit trail for all AI interactions
- ›Customer financial data protection at AI prompt layer
- ›Integrate with financial DLP for regulatory data leakage control
- ›MAS FEAT + MAS/OJK + MAS/BSP compliance documentation for AI
- ›AI vendor third-party risk assessments (regulatory approval)
- ›Automated AI usage policy enforcement across all departments
- ›AI governance committee with CISO & Chief Risk Officer
- ›AI incident response playbook (regulatory notification obligations)
- ›Real-time financial data loss prevention via AI gateway
- ›MAS/BSP & MAS/OJK AI governance reporting to board
- ›Predictive risk scoring for new FinAI tool onboarding
- ›Automated MAS FEAT / MAS AI audit evidence generation
- ›ISO/IEC 42001 + NIST AI RMF banking compliance readiness
- ›Threat model credit, fraud & KYC/AML AI systems
- ›Data lineage & provenance framework for training data
- ›Model fairness & bias assessment process established
- ›MAS FEAT + MAS AI AI security requirements definition
- ›Explainability requirements for regulatory model queries
- ›SentraRed adversarial testing of fraud & credit models pre-production
- ›SentraGuard inline for all customer-facing banking AI APIs
- ›RAG security controls for internal knowledge & policy AI
- ›KYC/AML model access controls & data access governance
- ›Model supply chain verification for third-party ML components
- ›Quarterly red team on production banking AI systems
- ›AIThreatIntel integrated for FinTech AI CVEs & MITRE ATLAS TTPs
- ›Model explainability documentation for MAS FEAT/MAS/OJK regulatory queries
- ›Financial AI incident response playbook (regulatory notification)
- ›Automated compliance evidence for MAS FEAT / MAS AI audits
- ›Banking AI Centre of Excellence with embedded compliance
- ›Zero-trust financial AI architecture across all deployments
- ›Autonomous fraud pattern adaptation & threat response
- ›Board AI Risk Committee with CISO & CRO reporting
- ›MAS FEAT + MAS/OJK + ISO/IEC 42001 external audit ready
- ›Inventory AI tools accessing operational & SCADA-adjacent data
- ›Classify data sensitivity: production data, reservoir models, maintenance logs
- ›Draft AUP for field, operations & engineering staff
- ›Identify OT-adjacent data paths to cloud AI platforms
- ›Block AI access to classified operational & safety systems data
- ›Deploy AI gateway for engineering & maintenance AI tools
- ›Automatic operational data protection at AI prompt layer
- ›MAS AI-compliant audit trail for OT-adjacent AI usage
- ›Air-gap controls for field & operational AI tools
- ›Integrate with OT data classification for upstream data control
- ›MAS AI compliance documentation for AI tool usage across sites
- ›Operational AI vendor risk assessments (supply chain focus)
- ›AI governance integrated into HSE management system
- ›Automated policy enforcement across all engineering AI tools
- ›AI incident response with production safety implications covered
- ›Predictive threat intelligence for energy sector AI attacks
- ›Board-level OT AI governance & risk reporting
- ›Automated MAS AI / IEC 62443 audit evidence generation
- ›AI security integrated into site safety case documentation
- ›ISO/IEC 42001 readiness for operational AI systems
- ›OT network segmentation for AI systems in ICS/SCADA environments
- ›Threat model operational AI: predictive maintenance, pipeline monitoring
- ›Training data governance for operational & sensor data
- ›IEC 62443 + MAS AI security requirements for OT AI systems
- ›Safety integrity level (SIL) requirements for AI in critical control
- ›SentraRed testing of operational AI models before production deployment
- ›SentraGuard for AI APIs connected to operational & SCADA data
- ›OT AI integrity monitoring: anomaly detection for SCADA AI outputs
- ›Supply chain security for OT AI components & model libraries
- ›Air-gapped deployment options for highest-criticality OT AI
- ›Quarterly red team on production OT AI systems (non-disruptive)
- ›AIThreatIntel integrated for OT/ICS-specific CVEs & MITRE ATLAS TTPs
- ›OT AI incident response playbook (production & HSE safety implications)
- ›IEC 62443 compliance evidence generation for OT AI controls
- ›Continuous monitoring of AI outputs against operational safety thresholds
- ›OT AI Centre of Excellence — security, HSE & operations integrated
- ›AI-powered operational resilience: predictive threat for critical infrastructure
- ›Zero-trust AI architecture across all OT environments
- ›Autonomous anomaly detection & response for SCADA AI systems
- ›IEC 62443 + MAS AI + ISO/IEC 42001 external audit ready
SentraSuite — AI Security Platform | SOAISEC Labs | SEA & Singapore Edition
