Accelyst AI Shield Capabilities
A detailed breakdown of every capability across all seven phases of the Accelyst AI Shield security program lifecycle.
AI Discovery & Risk Assessment
Understand what you have, where the risks are, and what matters most.
AI Asset Inventory
Comprehensive discovery and cataloging of all AI/LLM use cases, models, agents, MCP servers, plugins, embeddings, and data flows across your organization — including shadow AI and vendor-controlled AI embedded in third-party platforms.
AI Threat Modeling
Systematic identification of AI-specific attack vectors using OWASP LLM Top 10 and MITRE ATLAS frameworks. Produces prioritized risk register with severity scoring.
Framework Risk Mapping
Map identified risks to established compliance frameworks — NIST CSF 2.0, NIST 800-53, ISO 27001 — creating a unified risk view that satisfies multiple audit requirements.
Secure AI Architecture & Design
Build security into your AI systems from the ground up — not bolted on after launch.
Secure Deployment Patterns
Architecture reviews and reference designs for secure model deployment, orchestration, and multi-model coordination. Covers containerization, network isolation, and inference security — including on-premises and air-gapped environments.
Guardrails & Filtering
Input validation, output filtering, and content guardrails to prevent prompt injection, data leakage, and harmful outputs. Includes explainability layers for regulated decision-making.
Identity & Secrets Management
Authentication, authorization, RBAC, and secrets management for AI pipelines — ensuring model access, API keys, and inference endpoints are properly secured.
AI Governance, Policy & Compliance
Policies your legal team will approve. Audit trails your regulators will accept.
AI Usage & Data Governance
Enterprise-wide AI usage policies covering acceptable use, data classification, model lifecycle governance, and responsible AI principles tailored to your industry and regulatory context.
Regulatory Alignment
Gap analysis and control mapping for HIPAA, GDPR, SOC 2, NIST AI RMF, California AI Transparency requirements, and industry-specific AI regulations. Produces remediation roadmaps with clear ownership and timelines.
Audit Logging & Traceability
Comprehensive audit trail implementation — every model decision, prompt, parameter, and output logged with full traceability. Ready for internal audit and regulatory examination.
Data Lineage & Model Decision Logic
Immutable, cryptographically signed chain-of-custody records for every AI inference. Traces each input through retrieval steps, model API calls, and output generation — capturing the full reasoning path. Enables the organization to reconstruct exactly why a model reached a specific output if a decision is ever challenged or audited.
Secure Implementation & Hardening
Security baked into every line of code and every pipeline stage.
DevSecOps for AI Pipelines
Secure SDLC integration for AI development workflows — from model training to deployment. CI/CD pipeline hardening with security gates at every stage.
Code & Dependency Scanning
SAST, DAST, IAST, and SCA scanning adapted for AI components — covering model code, training scripts, inference endpoints, and third-party model dependencies.
LLM Red-Teaming & Agent Simulation
Adversarial testing for LLM systems and agentic AI — prompt injection, jailbreaks, RAG pipeline poisoning, data extraction, MCP server exploitation, and agent-to-agent privilege escalation testing.
Continuous Monitoring & Assurance
Real-time visibility into how your AI systems behave in production.
Runtime Monitoring
Continuous monitoring for model misuse, behavioral drift, output anomalies, and policy violations. Role-specific dashboards for security analysts, compliance officers, risk managers, and developers.
SIEM Integration & Alerting
Integration with your existing SIEM platform for centralized AI security event management. Anomaly detection, correlation rules, and escalation workflows tuned for AI-specific threats — 90-second detection-to-resolution for prompt injection events.
AI Incident Response & Recovery
When something goes wrong, your team knows exactly what to do.
AI Incident Response Playbooks
Pre-built and customized incident response playbooks for AI-specific scenarios — prompt injection breaches, model poisoning events, data exfiltration, and adversarial attacks.
Forensics & Remediation
AI-specific forensic investigation capabilities — containment procedures, root cause analysis, evidence preservation, remediation, and stakeholder communication protocols.
Continuous Improvement
Security is never done. Your program evolves as the threat landscape does.
Threat Re-evaluation
Periodic reassessment of the AI threat landscape, guardrail effectiveness, and control adequacy. Updates risk registers and remediation priorities as new attack vectors emerge.
Audit Readiness & Optimization
Ongoing audit preparation — documentation updates, control testing, evidence collection automation, and maturity model advancement to strengthen your security posture over time.
Start with a Security Assessment.
We'll map your AI footprint, model your threats, and deliver a prioritized roadmap — in 2–4 weeks.