123-456-7890
500 Terry Francine St. San Francisco, CA 94158
© 2025 by SYplat
AI-based semantically inter-connected security and governance unified platform for AI-driven enterprises
Through the SY platform, as a unified security control plane, we are committed to empowering enterprises to harness AI to secure and govern their entire IT landscape through semantically connected context—built from the enterprise’s own security data— enabling humans and AI to collaborate seamlessly to drive business value.
At the same time, the SY platform itself manages the security of both human and AI actors (such as AI agents) throughout their lifecycles.
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By fostering long-term partnerships with the ecosystem players and the enterprises, we aim to be a trusted ally in your journey towards enhanced security, compliance, and risk management.
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SY platform's envisioned capabilities prepare enterprises for the era where humans and AI-actors work together for your business success.
Identity Lifecycle Management
Autonomous Unified Lifecycle Governance of Human & AI actors
Autonomously establish a single, policy-driven identity plane for both workforce (human users) and AI actors (agents & services) . Govern AI-driven lifecycle management, enforcing real-time observability and accountability till offboarding or reboarding (of AI actors), with your policy adherence and real-time audit-ready evidence - with human actors as command center.
All these, working with your deployed solution.
Human–Agent Delegation & Assignment Lifecycle
Establish and govern the stewardship links between workforce identities and AI actors. Bind each assignment to a delegating owner with purpose, scope, and TTL; enforce real-time autonomous attestation and periodic human attestation and re-assignment on organization-driven change; cascade deprovisioning on exit; and retain a tamper-evident chain of accountability—reducing orphaned agents, tightening control effectiveness, and accelerating audit readiness.
Just-in-Time & Scoped-for-Intent Least Privilege with SoD Enforcement
Issue time-bound, task-scoped access to AI actors while enforcing real-time segregation-of-duties controls grounded on your policy/rule-set, compliance and regulation needs. Automate approvals with justification, prevent toxic combinations across human/agent permutations, and rollback privileges on completion—cutting standing access and compliance exceptions.
Policy-Aware Data Access & Minimization for Agents
Enforce classification-, purpose-, and jurisdiction-aware data retrieval so agents only access what is necessary, when necessary. Apply tokenization/pseudonymization at source, restrict cross-border flows to meet residency rules, and log immutable evidence—reducing data-exposure risk while strengthening adherence to regulations and internal policy compliance.
Human-in-the-Loop Oversight & Exception Management
Orchestration agents embed four-eyes approvals, reason-coded justifications, and time-boxed escalations for human or agent-initiated changes. Temporary elevation is auto-revoked and attestations are generated—tightening control effectiveness without slowing delivery.
End-to-End Provenance & Continuous Assurance
Bind every agent action to a delegating human or service and record tamper-evident provenance for both human and AI activity triggered on human command. Serve evidence-on-demand at real-time, score autonomous compliance, and let you trigger AI actor to contain when policy/regulatory/compliance thresholds are breached.
Access Control & Authorization
Unified Access Graph
Unify access for people and AI actors on one knowledge graph—a live map of identities, roles, policies, and entitlements. Cut blind spots, speed audits, and tighten control decisions. AI agents keep the graph fresh and orchestrate lifecycle and evidence across apps, data, and processes.
Autonomous Least-Privilege
Achieve least-privilege by default with risk-aware, just-in-time access and automatic expiry. Reduce standing rights, provisioning delays, and review workload while shrinking attack surface. AI agents grant, renew, and revoke based on purpose, context, and risk captured in the knowledge graph.
Live SoD Prevention
Stop Segregation-of-Duties breaches in flight. Graph-aware AI agents detect toxic combinations before approval, simulate impact, and enforce guardrails—reducing fraud exposure and remediation cycles without slowing the business. People and AI actors are governed together with explainable decisions.
Autonomous Attestations
Move from periodic certifications to continuous, risk-based attestations. AI agents pre-collect evidence, prioritize exceptions, and close the loop—cutting audit cycle time and improving control effectiveness. People and AI actors are reviewed together with traceable justifications and outcomes.
Delegation & Assignment Guardrails
Govern human–AI delegation lifecycle end-to-end—owner, purpose, scope, TTL, attestations, and cascade revocation. Ensure temporary authority delivers outcomes, then disappears on schedule or trigger. AI agents orchestrate setup, monitoring, renewals, and clean removal across systems.
Safe Break-Glass Control
Enable safe emergency elevation with predefined scopes, session monitoring, and automatic rollback. AI agents contain exposure and retire elevation instantly—reducing MTTR without residual risk. People and AI actors follow the same governed path with tamper-evident records.
External & Service Access
Apply the same controls to vendors, contractors, service accounts, bots, and APIs. AI agents onboard, rotate secrets, enforce least-privilege, and monitor behavior—reducing third-party and machine identity risk while maintaining provable evidence across the knowledge graph.
Graph-Driven Policy Control
Make authorization consistent and explorable across systems. AI agents maintain graph-linked policies, detect conflicts, and simulate change impact—lowering rollout risk, policy drift, and tickets. People and AI actors follow the same explainable, testable rules.
Risk Management
Continuous Risk Sensing
Spot issues early across humans and AI actors. Agents watch events, transactions, and model outputs on a single knowledge graph, linking weak signals to material risks and triggering policy, access, evidence, and containment flows—reducing incident likelihood and detection-to-action time.
Control Assurance Automation
Raise control effectiveness while lowering effort. Agents continuously test controls, gather evidence, and file attestations on the graph—shrinking audit cycles, reducing sampling blind spots, and keeping issues, owners, and fixes traceable across people and AI systems.
Policy-to-Risk Traceability
Turn policies into enforceable outcomes. The graph maps policy clauses to risks, controls, owners, and runtime signals; agents validate coverage, flag gaps, and route remediations—maintaining an always-current line-of-sight from risk appetite to operational behavior.
Access & Privilege Risk
Contain toxic access before it bites. Agents detect SoD conflicts, risky entitlements, and privilege drift for humans and AI actors, orchestrating approvals, TTLs, and revocations—keeping access aligned to purpose with evidence for every decision.
Third-Party & Model Risk
Manage vendor and AI model exposure as one. The graph connects suppliers, datasets, models, and services; agents monitor changes, evaluate controls, and trigger containment—reducing downstream risk from dependencies and generative outputs.
Incident Orchestration & Containment
Shorten dwell time when things go wrong. Agents correlate signals, freeze risky privileges, quarantine processes, and launch playbooks—coordinating humans and AI responders on the graph with complete evidence and post-incident learning loops.
Human–AI Delegation Governance
Make delegation safe by design. Agents enforce owner, purpose, scope, and TTL for assignments between people and AI actors, collect attestations, and cascade revocations—keeping accountability clear and reducing unintended actions.
Regulatory Reporting & KRIs
Report once, reuse everywhere. Agents assemble evidence, metrics, and KRIs directly from the graph, aligning to frameworks and audits—cutting reporting effort while improving accuracy and explainability.
Audit Management
Focused Scoping
Shrink planning cycles with graph-driven scoping and sampling. AI governance agents use live process and risk signals to propose scope, governing humans and AI actors together for defensible, faster starts.
Reusable, Private Evidence
Agents compile provenance-rich evidence once on the knowledge graph, apply masking and TTL, and reuse across frameworks and regulator formats. Management assertions and PBC packs are generated on demand from the same source.
Policy-to-Risk Traceability
Turn policies into enforceable outcomes. The graph maps policy clauses to risks, controls, owners, and runtime signals; agents validate coverage, flag gaps, and route remediations—maintaining an always-current line-of-sight from risk appetite to operational behavior.
Access & Privilege Risk
Contain toxic access before it bites. Agents detect SoD conflicts, risky entitlements, and privilege drift for humans and AI actors, orchestrating approvals, TTLs, and revocations—keeping access aligned to purpose with evidence for every decision.
Third-Party & Model Risk
Manage vendor and AI model exposure as one. The graph connects suppliers, datasets, models, and services; agents monitor changes, evaluate controls, and trigger containment—reducing downstream risk from dependencies and generative outputs.
Incident Orchestration & Containment
Shorten dwell time when things go wrong. Agents correlate signals, freeze risky privileges, quarantine processes, and launch playbooks—coordinating humans and AI responders on the graph with complete evidence and post-incident learning loops.
Human–AI Delegation Governance
Make delegation safe by design. Agents enforce owner, purpose, scope, and TTL for assignments between people and AI actors, collect attestations, and cascade revocations—keeping accountability clear and reducing unintended actions.
Regulatory Reporting & KRIs
Report once, reuse everywhere. Agents assemble evidence, metrics, and KRIs directly from the graph, aligning to frameworks and audits—cutting reporting effort while improving accuracy and explainability.
Data Protection & Privacy
Unified Data Inventory & Classification
Reduce blind spots and false positives; accelerate audits and control testing. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents maintain a live data map and classify sensitive fields using graph context, joining scanners with business usage.
Purpose & Lawful Basis Enforcement
Prevent unlawful processing; cut policy violations; speed evidence creation. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents bind lawful basis, consent, and declared purpose to identities, datasets, and workflows; runtime checks block misuse and capture evidence for auditors.
Subject Rights & RTBF Automation
Shorten DSAR turnaround from weeks to hours; improve completeness. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents traverse lineage to find all records for a subject, orchestrate deletions or restrictions, and log verifiable evidence of completion.
Retention, Holds &
Minimization
Reduce storage and legal risk while preserving operational evidence. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents reconcile retention schedules with legal holds, apply selective redaction in logs and evidence, and minimize data footprint without breaking operations.
Privacy by Design & DPIA Automation
Fewer late-stage surprises; faster releases with provable compliance. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents pre-flight changes against DPIA templates tied to components, flag risks, propose mitigations, and prevent deployment until controls are satisfied.
AI Training Data Governance
Lower privacy risk in ML; speed safe model promotion. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents track dataset→feature→model lineage, quantify PII exposure, enforce de-identification, and gate model promotion with privacy checks.
Cross-Border & Processor Governance
Avoid transfer violations; keep third-party risk continuously in check. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents enforce jurisdictional routing, verify DPA obligations across processors, and stop illegal transfers while collecting machine-verifiable evidence.
Human–Agent Delegation Lifecycle
Eliminate orphaned access; ensure accountable AI operations end-to-end. Governance covers humans and AI actors on a single knowledge graph. Autonomous governance agents apply controls, capture evidence, and contain drift. Agents govern delegation links—owner, purpose, scope, TTL, attestations—with auto-expiry, continuous use monitoring, and cascade revocation.