Service
Shadow AI deployments create governance blind spots that introduce severe data leakage and compliance risks.
Individual teams deploying AI without oversight create a fragmented, ungoverned attack surface. This shadow AI exposes you to:
Our AI Security Posture Management (AI-SPM) Integration service moves you from reactive detection to active control. We implement platforms like Wiz or Palo Alto to:
Centrally enforce policy-as-code, score AI asset risk, and maintain continuous compliance monitoring.
This provides the technical foundation for our broader Shadow AI Detection and Security Posture Management pillar, enabling a unified governance layer across all AI initiatives, including Sovereign AI Infrastructure and Enterprise AI Governance.
Integrating a dedicated AI Security Posture Management platform transforms shadow AI from a governance blind spot into a managed, secure asset. Our implementation service delivers measurable business results.
We implement unified guardrails across all sanctioned and unsanctioned AI assets, enabling consistent application of data handling, access control, and compliance policies from a single pane of glass.
Move from detection to active risk management with continuous scoring of AI deployments. We provide CTOs with a prioritized, evidence-based remediation roadmap, directly quantifying financial and compliance exposure.
We configure AI-SPM platforms to generate automated audit trails mapping AI data flows to specific articles of GDPR, HIPAA, and the EU AI Act, turning compliance from a manual burden into a continuous output.
Gain granular visibility into AI service consumption across AWS Bedrock, Azure OpenAI, and other providers. We enable accurate showback, eliminate waste from redundant models, and optimize cloud spend.
By providing a governed, low-risk environment for innovation, we enable engineering teams to safely experiment with and deploy new AI capabilities, reducing time-to-value for sanctioned projects. Learn more about enabling secure innovation with our Agentic Workflow Design and Integration services.
A phased breakdown of a standard enterprise AI-SPM integration project with Inference Systems, detailing key deliverables, responsibilities, and typical duration for each stage.
| Phase & Key Activities | Inference Systems Deliverables | Client Responsibilities | Typical Duration |
|---|---|---|---|
Phase 1: Discovery & Inventory | Comprehensive network scan report Shadow AI risk heat map Initial policy gap analysis | Provide network access & API credentials Identify key stakeholder teams | 1-2 weeks |
Phase 2: Platform Integration & Configuration | Deployed AI-SPM platform (Wiz, Laminar, etc.) Customized policy rules & risk scoring Integration with existing SIEM/SOAR | Approve policy rule sets Provide access to security tools for integration | 2-3 weeks |
Phase 3: Pilot & Validation | Monitored pilot environment with live alerts Validation report on detection accuracy Refined DLP & Copilot fencing policies | Designate pilot user group Validate alerts and policy effectiveness | 1-2 weeks |
Phase 4: Enterprise Rollout & Training | Full-scale deployment across approved environments Administrator & security team training sessions Operational runbooks and escalation procedures | Coordinate internal communications Schedule team training sessions | 1 week |
Phase 5: Ongoing Management & Optimization | Monthly compliance & risk reports Quarterly policy reviews & optimization Access to our AI red teaming expertise | Designate ongoing platform owner Participate in quarterly reviews | Ongoing (SLA) |
Total Project Timeline (Typical) | 4-8 weeks to active management |
We implement AI-SPM not as a point tool, but as an integrated layer of your security fabric. Our four-phase methodology ensures rapid deployment, comprehensive coverage, and measurable risk reduction.
We deploy passive and active network scanning to build a real-time inventory of all AI tools, models, and API endpoints—sanctioned and unsanctioned. This provides the foundational visibility required for governance, directly addressing the core challenge of Shadow AI Detection and Security Posture Management (AI-SPM).
Our team conducts a technical evaluation of discovered AI assets to quantify data leakage, compliance violations, and operational risks. We provide a prioritized, evidence-based remediation roadmap with financial exposure analysis, a critical step before AI-SPM Integration with SIEM/SOAR.
We configure and integrate leading AI-SPM platforms (Wiz, Laminar, Palo Alto) to centralize policy-as-code, enforce data loss prevention (DLP) rules, and apply risk scoring. This moves your posture from detection to active management, enabling AI Copilot and Assistant Usage Fencing and other critical controls.
We establish continuous monitoring, audit trails, and automated reporting mapped to regulatory frameworks like GDPR and HIPAA. This ensures ongoing compliance for AI-SPM for Regulatory Compliance (GDPR, HIPAA) and integrates alerts into your existing Enterprise AI Governance and Compliance Frameworks.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Get clear answers on the process, timeline, and outcomes of integrating a dedicated AI Security Posture Management platform into your enterprise environment.
We follow a phased, risk-based methodology proven across 50+ enterprise AI security projects. Phase 1 involves discovery and inventory using our Shadow AI Discovery service. Phase 2 is platform integration and policy mapping, where we configure your chosen AI-SPM tool (Wiz, Laminar, Palo Alto) to your specific risk framework. Phase 3 includes validation, user acceptance testing, and handover with documented runbooks.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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