Snyk saw this gap early. Securing AI-native systems requires a new model, one that combines visibility, governance, and trust. Snyk defines that model as AI Security Posture Management (AI-SPM), powered by a foundational artifact: the AI Bill of Materials (AI-BOM).
AI-SPM provides the framework for managing AI risk; the AI-BOM delivers the evidence that makes it real, turning AI from something opaque into something observable and governable.
Together, they enable enterprises to innovate confidently by knowing exactly where AI runs, what it relies on, and how securely it operates.
What is AI-SPM and why does it matter?
AI-SPM extends Application Security Posture Management to systems built with large language models, generative frameworks, and agentic logic. It provides teams with continuous visibility into every AI component, including models, datasets, prompts, embeddings, and APIs, and assesses how those components behave in real-world conditions.
Unlike traditional SPM, AI-SPM evaluates behavioral risk, not just known vulnerabilities. It detects data leakage, prompt injection, and model drift. It doesn’t stop at testing; it enforces adaptive guardrails and correlates results with policy and compliance needs.
This approach transforms AI from a black box into an observable, governable system, one that can be validated, monitored, and trusted.
Discovery and visibility: The starting point for AI-SPM
Every effective AI security strategy begins with visibility. Before you can manage risk, you need to know where AI is running, and most organizations don’t. Models, datasets, and agent frameworks often slip into projects through quick experiments or local installs, creating layers of hidden or “shadow AI” that traditional tools can’t detect.
Continuous discovery is the foundation of AI-SPM. It identifies all AI assets, including models, datasets, prompts, MCP servers, and agents, across your environment and reveals how they are connected. This process produces the AI-BOM, a structured inventory of every AI component in use. Much like a Software Bill of Materials (SBOM) in software security, AI-BOM turns guesswork into governance by making unseen dependencies visible.
For existing Snyk customers, this visibility comes automatically. The AI-BOM can be generated from code already onboarded through Git repository integrations, providing teams with an instant view of their AI footprint without any additional setup. Discovery may not be the flashiest part of AI security, but it’s the most important because you can’t secure what you can’t see.
Use case: The boardroom question — Enterprise-wide model inventory
Scenario: During a quarterly executive meeting, the board asked a simple but critical question: “Which AI models are we using across our applications?”
The CIO could answer:
Number of repositories
Number of libraries
Number of open vulnerabilities
…but could not answer which models were actually deployed, where, or by whom.
Impact: The board realized the company was effectively blind to its AI supply chain. Any audit or compliance review would be impossible, and the organization had no ability to govern which models were in use.
Solution with AI-BOM / AI-SPM:
Enterprise-wide inventory: AI-BOM provides a full catalog of all models deployed across agents, pipelines, and MCP servers.
Policy & compliance controls: AI-SPM lets executives attach organizational policies to each model (approved, restricted, disallowed).
Automated reporting & remediation: Any policy violation is flagged, and remediation actions can be automatically executed — from pipeline blocking to Jira ticket creation.
Outcome: Leadership now has confidence in their AI risk posture. They can make strategic decisions, enforce compliance, and scale AI adoption securely across the enterprise.
The maturity path of AI-SPM
Snyk frames AI-SPM as a maturity journey, similar to DevSecOps but optimized for AI’s dynamic nature.
Stage 1: Inventory and visibility
Security begins with understanding the existing AI assets. Snyk’s AI-BOM CLI automatically scans source code and pipelines to detect embedded models, datasets, and agent frameworks. It surfaces version data, licenses, and dataset linkages that would otherwise remain invisible.
Stage 2: Risk assessment and correlation
Visibility must lead to understanding. AI-SPM correlates inventory findings with Snyk’s Model Risk Database, which benchmarks open source LLMs against OWASP LLM Top 10 threats like prompt injection and data exfiltration.
The database is continuously updated using telemetry from public model repositories, threat research, and community findings, giving teams real-time context on how model behaviors evolve. This correlation transforms discovery into context, linking every model and dataset to quantifiable security posture.
Stage 3: Policy enforcement and guardrails
Governance follows understanding. Snyk’s Snyk Guard agents automate policy enforcement, ensuring that unapproved models or risky datasets cannot be deployed. Guardrails adapt dynamically as new AI assets appear.
Stage 4: Continuous validation and AI-TrustOps
At full maturity, AI-SPM incorporates automated red teaming. Snyk’s AttackAgent fuzzes prompts and simulates adversarial attacks to expose vulnerabilities before they reach production. The system continuously re-scores posture based on observed behavior, closing the loop between detection, enforcement, and improvement.
Turning AI-BOM into guardrails
Visibility is only the beginning. The true value of the AI-BOM emerges when it fuels automated controls that turn insight into action. Each record becomes a policy signal for AI-SPM, enabling Snyk Guard to block builds with unapproved or unlicensed models, flag risky datasets or prompt libraries, enforce role-based access for model APIs, and detect fine-tuning on restricted data. Together, these capabilities form an active control plane, an evolving connection between what’s built and what’s allowed.
Regulatory readiness also depends on this level of visibility. Frameworks like the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework all require documentation of model lineage, data usage, and safety controls. The AI-BOM provides that evidence automatically, showing not just what is running but how it aligns with internal and external requirements.
Unlike a static SBOM export, AI-BOMs are continuously refreshed. Every new commit or dependency update re-evaluates posture and policy, transforming compliance from a periodic checkbox into an ongoing assurance mechanism that keeps pace with development.
Use case: The DeepSeek commit — Preventing rogue models in repos
Scenario: A Fortune 100 security team was surprised by a single line of code in a critical repository. No ticket. No review. No approval. This developer had simply tested a new AI model locally before pushing their changes.
Impact: The team only discovered the model five days before release, when legal flagged a potential licensing issue buried in the pull request. The CISO realized:
“We have SBOM. We have SAST. But we don’t have visibility into models. We don’t even know what AI our developers are using.”
Solution with AI-BOM / AI-SPM:
Inventory & discovery: AI-BOM scans repositories, pipelines, and agent activity to identify the presence of models like DeepSeek.
Policy enforcement: AI-SPM allows the security team to attach rules — e.g., “Deny DeepSeek” — so future commits are automatically blocked or flagged.
Audit & remediation: Every detection is logged, creating an audit trail for compliance teams, and developers receive clear guidance on alternatives.
Outcome: Rogue models never make it into production, compliance violations are prevented, and engineering teams can experiment with AI safely under governance.
Use case: Shadow AI — Tracking unknown models in the pipeline
Scenario: At a mid-size enterprise, engineers noticed performance drops in their AI pipeline. Meanwhile, legal raised concerns about license violations.
The culprit: a new LLM had been introduced via a copy-pasted API key and wired into a local MCP server, entirely bypassing the normal dependency tracking process. Traditional SBOMs never detected it.
Impact: Multiple teams were affected:
Security: No prior knowledge of the model’s presence.
Engineering: Unplanned load and performance issues.
Legal/compliance: Exposure to licensing violations.
Solution with AI-BOM / AI-SPM:
Agent & MCP scanning: AI-BOM discovers models deployed dynamically via agents or MCP servers, even if they never enter the code repository.
Visibility dashboard: Security teams can see every model in use across pipelines, agents, and endpoints — a “model-first” view.
Enforcement & governance: AI-SPM applies policies automatically, flagging unapproved models and preventing them from being used downstream.
Outcome: Shadow AI is eliminated. Teams gain full visibility into every model in use, reducing compliance risk, and engineers can innovate without inadvertently introducing unknown AI into production.
Governance and compliance
The AI-BOM serves as the connective layer between AI security and governance. By mapping the who, what, and how of every model, dataset, and tool, it builds a reliable foundation for compliance across evolving frameworks. It also connects directly to global standards, helping teams demonstrate accountability and readiness across multiple requirements:
EU AI Act: AI-BOM supports traceability requirements by mapping model lineage, dataset sources, and version history.
ISO/IEC 42001: It provides the evidence required for an AI management system certification, demonstrating accountability, documentation, and control.
NIST AI RMF: AI-BOM directly supports the “Map” and “Manage” functions, offering measurable insight into AI components and associated risks.
The goal isn’t to anticipate every regulation but to stay prepared as they evolve. With AI-BOM visibility, organizations already hold the evidence component lineage, policy enforcement logs, and behavioral validation data needed to prove responsible AI operations. This readiness shifts compliance from a reactive task to an active capability that builds confidence across teams and regulators alike.
Why this matters for secure development
Without AI-SPM, organizations are effectively flying blind. AI systems evolve faster than traditional AppSec tools can observe. Without visibility into models, datasets, and prompt logic, even well-intentioned innovation can introduce hidden vulnerabilities or compliance risk.
AI-SPM changes that equation. It makes AI systems observable, measurable, and governable across the entire SDLC from development to deployment. It helps teams detect drift, prevent data leakage, and prove accountability with verifiable evidence.
By pairing AI-SPM with AI-BOM, Snyk gives developers the same trust model that transformed software security a decade ago: develop fast and stay secure.
Snyk’s developer-first approach ensures these capabilities integrate directly into workflows with no friction and no delay. And because the AI-BOM CLI is available now through Snyk Labs, every organization can start building its own path to AI trust today.
AI-SPM defines the discipline. AI-BOM delivers the proof. Together, they establish the foundation for securing, measuring, and governing modern AI systems.
Explore AI-SPM and try it yourself at Snyk Labs.
