The Enterprise AI Security, Compliance & Cyber Resilience Platform
THE PROBLEM
AI is evolving faster thanenterprise security can keep up.
AI has become part of applications, business workflows, SaaS platforms, and decision-making systems—but most security programs were never designed to secure this new ecosystem. Organizations need visibility not just into where AI exists, but what it can access, how it can be attacked, and whether it remains secure and compliant in real time.
- 1
AI Assets Are Invisible and Difficult to Govern
Organizations lack complete visibility into AI models, agents, APIs, datasets, and Shadow AI.
- 2
AI Data & Access Risks Are Unclear
Sensitive data flows through AI systems without clear visibility into access, permissions, or exposure.
- 3
AI Systems Are Exposed to New Attack Techniques
Prompt injection, jailbreaks, poisoning, manipulation, and agent abuse bypass traditional security controls.
- 4
AI Security Requires Continuous, Real-Time Risk Detection
Constantly changing AI environments require continuous monitoring, threat detection, and real-time protection.
- 5
AI Vulnerabilities Remain Undiscovered Without Red Teaming
Red teaming identifies exploitable AI weaknesses before attackers discover and abuse them.
- 6
AI Compliance & Governance Are Becoming Mandatory
Evolving regulations require continuous AI governance, risk assessment, transparency, accountability, and compliance evidence.
A REAL-WORLD AI ATTACK SCENARIO
Attacker
Uses a crafted prompt to manipulate the AI agent
LLM(Foundation Model)
MCP Server(External Tools)- Vector Database(RAG / Knowledge)
- Connected Apps(SaaS, Cloud, APIs)
AI Agent
Processes requests and uses connected tools
- Sensitive Data AccessedCustomer data, source code, internal documents
- Unauthorized ActionsExecutes commands, accesses tools and APIs
- Data ExfiltrationSends data to external endpoint
THE EXPANDING AI ECOSYSTEM
- LLMs
- AI Agents
- MCP Servers
- Vector Databases
- Prompt Libraries
- AI APIs
- Fine-Tuned Models
- SaaS AI
- Shadow AI
- Training Datasets
- AI Workflows
Secure your entire AI ecosystem
Six capabilities working as one — from discovery to remediation, purpose-built for modern AI workloads.
An illustration of AI Sentinel as a production line: an asset is found at the start, then graded, attacked, gated and filed as evidence, on one asset spine.
Grading runs across the posture surfaces Azure, AWS, GCP, Self-hosted, SaaS, Kubernetes, and every check returns Pass, Fail, Manual — a check that cannot read its evidence returns Manual, never a pass. Enforcement runs inline on the rails Prompt injection, PII in / out, Secrets, Output policy, Refusal quality, each of which can monitor, redact, block. Evidence is filed against MITRE ATLAS, EU AI Act 2024, NIST AI RMF 1.0, ISO/IEC 42001, OWASP LLM Top 10, and delivered to Splunk HEC, Webhook, Wazuh, IBM QRadar, ServiceNow.
- Discover — Find it. Find every model, endpoint, agent, MCP server, artifact and unsanctioned tool.
- Assess — Grade it. Grade the configuration, identity, data and dependency risk behind every asset.
- Test — Attack it. Attack it the way an adversary would, and grade the result defensibly.
- Enforce — Block it. Sit in the request path and block the attack before the model answers.
- Govern — Prove it. Turn all of it into evidence an auditor, a regulator and a board will accept.
One AI asset spine. One identity from discovery to evidence.
Every AI model, agent, API, and workflow discovered by Suronex is continuously mapped, assessed, red-teamed, governed at runtime, and captured as auditable evidence—creating a single source of truth from discovery to decision to defence.
Find every model, endpoint, agent, MCP server, artifact and unsanctioned tool.
- AI Asset Discovery & InventoryOne deduplicated spine for every AI asset
- AI-BOM & Model Supply ChainPayload analysis and a bill of materials
- Shadow AI DiscoveryIndependent vantage points on shadow AI
- SaaS & Enterprise AI GovernanceGovernance of the AI accounts you pay for
Grade the configuration, identity, data and dependency risk behind every asset.
- AI Security Posture ManagementPosture checks across managed AI services
- Self-Hosted & MLOps Host PostureAgentless fingerprinting of GPU and VM hosts
- AI Identity & EntitlementsAgents and keys as first-class identities
- AI Data SecurityWhat a model can reach, and what walks out
- ML Stack Vulnerability ManagementCVEs in the containers and VMs AI runs on
- AI Code & Repository SecurityAI-specific defects generic SAST misses
Attack it the way an adversary would, and grade the result defensibly.
- Behavioral Red TeamAdversarial search against a live endpoint
- Agentic & MCP Red TeamThe agent, its tools, and what it trusts
- Grading, Evidence & RegressionProving the verdict is right, and stays right
Sit in the request path and block the attack before the model answers.
- AI GatewayOne inline plane for every model call
- Runtime GuardrailsInspect, redact or terminate mid-response
- Agent Runtime & MCP ControlPre-execution control on the tool call
Turn all of it into evidence an auditor, a regulator and a board will accept.
- Compliance & AI GovernanceClauses fed by evidence, not a questionnaire
- Detection, Response & OperationsFindings become assigned work in your SOC
Every surface these five planes touch
AI doesn't run alone. Neither should your security.
Every AI workload depends on cloud infrastructure, identities, applications and data. Suronex extends protection across your complete technology stack.
One unified platform
Instead of 10 different security tools — a single source of truth from the AI layer down to infrastructure.
Full-stack context
Correlate an exposed prompt with the identity that owns it, the cluster it runs on, and the data it can reach.
Attack paths, visualised
See how a jailbroken agent could pivot into cloud resources — and cut the path before it's exploited.
Posture + runtime security
Configuration posture paired with live runtime detection at every layer — threats caught as they happen, not at the next scan.
Compliance built in
Every layer continuously mapped to AI and enterprise standards with audit-ready evidence.
One Platform. Complete Security for the AI-Driven Enterprise.
Every capability works from one asset graph, one data model, one risk engine and one unified dashboard—giving security teams a connected view of risk instead of another collection of siloed tools.
Built different. On purpose.
One Platform
Cloud, AI, compliance, identity, vulnerability and application security. One dashboard.
AI Native
Built for modern AI workloads — not adapted from legacy posture tools.
Compliance First
Continuous evidence collection. Audit-ready reporting. Real-time compliance.
Agentless
Deploy in minutes. No agents. No downtime.
AI Powered
AI assists with discovery, risk prioritisation, correlation, remediation and reporting.
Security Built for Every Industry
One platform. Every sector. Every critical risk.
From BFSI and healthcare to SaaS, e-commerce, telecom, manufacturing and energy, Suronex adapts to the security, compliance, AI and cloud risks unique to your industry—helping organisations secure their digital ecosystem and meet the standards that matter most.
800-53
CSF CSF CSF Every sector Suronex covers, what it weights first there, and the frameworks it maps.
- BFSI — Public exposure of systems handling payment, financial, credit and customer data — then excessive privileges and cloud misconfigurations. Frameworks: PCI DSS, DORA, RBI, SEBI CSCRF, SOC 2, ISO/IEC 27001:2022, NIST CSF, CIS.
- Healthcare — Uncontrolled access to PHI and PII, exposed healthcare workloads and insecure APIs — then the AI systems reaching sensitive patient data. Frameworks: HIPAA, HITRUST, EU GDPR, ISO/IEC 27001:2022, SOC 2, NIST CSF.
- Government — Data residency violations and sovereign data exposure — then internet-facing services, misconfigured cloud infrastructure and excessive privileged access. Frameworks: FedRAMP, NIST 800-53, DPDP Act, EU GDPR, CIS, NIST CSF, ISO/IEC 27001:2022.
- SaaS — Tenant isolation failures, exposed APIs and excessive third-party OAuth permissions — then secrets in code and insecure cloud workloads. Frameworks: CSA CCM v4, SOC 2, EU GDPR, ISO/IEC 27001:2022, NIST CSF, CIS.
- Manufacturing — Cloud-to-OT connectivity and exposed industrial workloads — then insecure identities, vulnerable systems and insufficient IT/OT segmentation. Frameworks: CIS, ISO/IEC 27001:2022, SOC 2, NIST CSF.
- Retail — Cardholder-data scope creep and exposed payment infrastructure — then customer PII, insecure APIs and third-party integrations. Frameworks: PCI DSS, DPDP Act, EU GDPR, SOC 2, CIS, ISO/IEC 27001:2022, NIST CSF.
- Telecom — Large-scale exposure of subscriber data and privileged access risk — then exposed network and cloud infrastructure and third-party ecosystem risk. Frameworks: DPDP Act, EU GDPR, NIST CSF, ISO/IEC 27001:2022, CIS.
From Connection to Confidence — One Platform, One Connected View
- 01
Connect Everything
Connect clouds, SaaS, code, identities, containers, AI services, data and workloads through a unified integration layer.
- 02
Discover & Map
Automatically discover and map your entire digital estate—from cloud assets and applications to AI models, agents, APIs, identities and data—into one connected asset graph.
- 03
Assess & Govern
Continuously identify misconfigurations, vulnerabilities, identity risks, AI threats, exposure and compliance gaps, correlated through a single risk engine.
- 04
Remediate & Prove
Get AI-guided remediation, prioritise what matters most, track risk reduction, and generate audit-ready and executive-level insights from one platform.
The steps are drawn as a conduit that climbs: a packet enters at the connector, is resolved into named assets, read against a threshold, and leaves under a closed seal. The connector gathers a field of connected cloud, identity, container, code and SaaS platforms over lanes for cloud accounts, SaaS organisations, code repositories and AI services.