AI Infrastructure Security
Secure the Agentic AI Stack
Most AI security stops at model behavior and prompt injection. We focus one layer down, where the durable risk lives: poisoned models, tampered pipelines, over-privileged agents, and a network perimeter they cross on every tool call.
Read-only · metadata-only · agent-aware · maps to NIST, OWASP & DORA
AI infrastructure security is fundamentally an identity, supply-chain, and runtime-enforcement problem.
What the agentic shift broke
Three things follow, and each one is invisible to the tooling enterprises already own.
The supply chain is the new dependency graph
Serialized model files can execute code, public registries get namespace-hijacked, and agent skills carry payloads written in natural language, not code. Traditional software-composition tooling doesn't see any of it.
The perimeter is gone
Autonomous agents act across cloud platforms, APIs, vector databases, and external tools, dissolving the perimeter most enterprises still rely on. Zero-trust has to be re-applied to non-human identities.
Frameworks exist; enforcement doesn't
Clients have the binders. What they lack is policy enforced at runtime against every request, and a clear answer to “whose job is this?” when AI security spans data science, platform, and infosec at once.
Not another “AI security” tool
The crowded lane is model behavior and content safety. We work the infrastructure underneath it.
DASP: the platform
One canonical model for your whole AI estate: discovery, assessment, supply chain, and governance.
Discover the AI estate
Read-only collectors map models, endpoints, agents, tools, MCP servers, registries, and the non-human identities they run as, across source trees, containers, and the cloud (ECS, SageMaker, Bedrock).
Assess agent identity & authorization
Flag static API keys, authorization that isn't scoped per tool, and over-privileged task roles. Delegation chains and the agentic blast radius, made visible.
AI Bill of Materials
Provenance per model: origin, base model, license, and training-data disclosure, pulled from Ollama and Hugging Face. Know your model supply chain.
Map to the frameworks you report against
Findings map to NIST AI RMF, OWASP LLM & Agentic Top 10, CSA, and DORA. A severity-weighted heat map shows where you're covered and where you're not.
Advisory lifecycle, owner-assigned
Every finding becomes an advisory an analyst can escalate to a ticket, mitigate, or close, each with an owner across the five domains. Decisions persist across runs and push to Jira.
Blast-radius reachability
Ask what a leaked credential can reach in N hops. Answered natively over the asset graph, so remediation targets the real exposure, not a guess.
A control plane, and collectors that meet your estate where it is
Built for many environments and many tenants: prod, staging, every account.
Collectors, where the targets live
A thin, read-only collector runs in each environment (an ECS task, a cluster, a host) and pushes a metadata-only asset graph outbound. No inbound port; no credentials leave the boundary.
Control plane assesses
The cloud control plane verifies each collector's workload identity, then assesses, maps to controls, scores, and tracks advisories, scoped per environment and per tenant. Update the logic centrally; never redeploy a collector.
Analysts govern
A console gives analysts the posture, the heat map, the AI BOM, and the advisory queue across every environment. Triage, escalate, and prove coverage to auditors.
The front door
AI Infrastructure Security Posture Assessment
A fixed-scope, fixed-fee engagement: a baseline of your AI supply chain, runtime, and agent surface against NIST AI RMF and OWASP. It ends in a prioritized, owner-assigned remediation roadmap, not a 200-page audit.
- 01
Discover the AI estate
Inventory models, datasets, pipelines, registries, vector stores, gateways, and deployed agents, then map the model & agent supply chain. Most clients have never seen theirs.
- 02
Assess the attack surface
Evaluate every layer (supply chain, MLSecOps pipeline, inference & runtime, RAG data layer, agent control plane) against known AI-specific vectors.
- 03
Map to frameworks, find the gaps
Benchmark against NIST AI RMF and OWASP LLM + Agentic Top 10. Translate each gap into a control, an owner, and a framework citation.
- 04
Prioritize & roadmap
Rank by exploitability × blast radius × effort. Sequence Foundational → Production-hardening → Scale, with ownership assigned across the five domains.
- 05
Readout
An executive readout for the board (risk narrative, top priorities) and a technical readout for the platform, ML, and security teams.
What you walk away with
- A defensible, framework-anchored view of AI infrastructure risk a CISO can take to the board.
- Clear ownership where there was none, resolving “whose job is this?” across five domains.
- A sequenced remediation plan instead of tool sprawl.
Synthesize the frameworks, don’t collect them
Every framework tells you what good looks like. None tells you who owns it, or how to enforce it at runtime. We map overlapping requirements once, and enforce once.
A security product that holds itself to its own standard
Read-only by construction
Only list / describe / inspect calls are ever issued. DASP observes; it never mutates the target, and never holds write credentials. Every external call is in a self-audit log.
Metadata, not payloads
Names, ARNs, locations, identities, and credential key names. Never secret values, model weights, or customer data. Enforced by tests.
We dogfood workload identity
Collectors authenticate with short-lived OIDC/SPIFFE workload identities, not static keys. That is the exact posture DASP flags everyone else for missing.
See your AI estate the way an attacker would.
Book a walkthrough of DASP, or scope a fixed-fee posture assessment against a representative environment.
Request a demoor email hello@downforcecyber.com