That volatility makes AI-native risk less about “hidden” threats and more about unknowns. Unknown components, unknown data flows, unknown behaviors that arise as systems learn, adapt, and integrate at unprecedented speed. These aren’t blind spots in the traditional sense; they’re areas of active discovery where existing frameworks simply don’t have the context to keep up.
The challenge, then, isn’t just to expose what’s hidden but to make the unknown knowable. To build visibility and governance into a space defined by change. In the sections that follow, we’ll explore how organizations can bring order to these moving parts, mapping models, plugins, and agents as living assets that can be monitored, secured, and evolved safely.
What makes AI-native attack surfaces hard to predict
AI-native applications are difficult to secure because their behavior can’t always be predicted. Traditional software follows defined logic; AI systems generate outputs probabilistically. The same input can yield different results depending on the context or training data. This non-determinism means that even well-designed models can behave in new, unexpected ways after deployment.
The complexity only grows as applications connect to retrieval APIs, vector databases, embeddings pipelines, plugins, and agent frameworks. Each new integration adds another entry point and potential trust gap. Within these systems, data flows are constantly shifting as context windows, prompts, and policies evolve, altering how information is processed and how it can be leveraged.
Worse, many of these components live outside your control. Model providers and plugin developers can push silent updates or change permissions without notice. What you tested last week may not be what’s running today. AI-native security transcends traditional defense paradigms, focusing on a moving target rather than fixed perimeters.
Concrete failure modes to watch
AI-native systems can fail in ways that look nothing like traditional software vulnerabilities. When components evolve faster than they’re monitored, small changes can cascade into large exposures. Some of the most common and consequential failure modes include:
Data poisoning: Occurs when tainted information makes its way into training sets, prompt libraries, or embeddings. Models can be manipulated to behave unpredictably or even maliciously. The result is subtle corruption that undermines accuracy, fairness, or trust without tripping obvious alarms.
Model inversion: By crafting specific inputs, attackers can coax a model into revealing sensitive details, training data, private prompts, or proprietary logic, turning the model itself into a source of leakage.
Evasion: Jailbreaks, policy bypasses, and prompt manipulation can circumvent established guardrails, resulting in unsafe or noncompliant responses that traditional filters may fail to detect.
Plugin or tool misuse: Agents equipped with broad permissions or loosely defined scopes can execute unintended functions, escalate privileges, or combine tools in dangerous ways that were never part of their original design.
Governance drift: Over time, small, undocumented changes, model swaps, quiet configuration edits, or untracked permission updates create uncertainty about what’s truly running in production and who controls it.
These risks serve as a reminder that AI-native systems aren’t compromised only through exploitation; they’re compromised through entropy. Without clear visibility and change tracking, what starts as innovation can gradually drift into exposure.
Unknown is not undefendable
The good news is that “unknown” doesn’t have to mean undefendable. The same qualities that make AI-native systems dynamic also make them observable if you have the right framework in place. That’s where AI Security Posture Management (AISPM) comes in.
AISPM acts as the control center for AI-native risk. Its purpose is to maintain a live inventory of all AI assets, data flows, and security controls across every environment. Instead of relying on static audits or manual documentation, it continuously updates as new models, plugins, or agents are introduced. This turns the AI stack from a black box into a transparent, traceable system that security and compliance teams can actually govern.
At the core of this visibility is the AI Bill of Materials (AI-BOM), a structured record that captures the moving parts of an AI system. A comprehensive AI-BOM includes:
Models and their versions
Prompts and policies
Datasets and lineage
Embeddings stores and vector databases
Plugins, tools, and agents
Retrieval paths and runtime configurations
Together, AISPM and the AI-BOM provide organizations with a comprehensive and durable map of their AI environment. They enable the tracing of data flow, identifying dependencies, and tracking changes. More importantly, they transform unknowns into managed risks, creating the visibility and control needed to secure AI systems as they evolve at the same speed.
Continuous discovery is the foundation of exposure management
AI systems evolve too quickly for static security practices to keep up. A single audit might capture the state of your environment today, but by tomorrow, a model could be retrained, a plugin swapped, or a retrieval route reconfigured. These systems don’t sit still, and neither should your defenses.
Continuous discovery changes the equation. Instead of relying on periodic checks, it maintains an active pulse on your AI ecosystem, watching for changes as they occur and understanding why. Think event-driven updates when models or datasets are modified, automated rescans to validate data integrity, and drift detection that flags unplanned adjustments to plugins, prompts, or policies. It’s a living inventory, not a static list.
The real power of this approach comes from turning visibility into direction. Not every change introduces the same level of risk, so continuous discovery works best when it’s tied to smart prioritization. By weighing data sensitivity, blast radius, and exploitability, teams can focus their efforts where they matter most, on the exposures that could truly disrupt operations or compromise trust.
When discovery is continuous and context-driven, exposure management becomes a proactive process. It becomes an ongoing conversation with your AI systems, one that keeps security aligned with the pace of innovation.
Mapping the AI-native stack: a practical framework
Once organizations adopt continuous discovery, the next step is learning how to map the AI-native stack in a structured and repeatable manner. Visibility isn’t just about knowing what exists; it’s also about understanding what is visible. It’s about understanding how those parts connect, evolve, and behave over time. A practical framework helps turn that visibility into control.
Step 1: Inventory: Start by cataloging everything that shapes your AI system: models and fine-tunes, prompts and policies, plugins and tools, agents, datasets, and retrieval components. This is your foundation, the raw material of the AI environment.
Step 2: Trace data flows: Next, follow the path of information through the stack. Map how data is ingested, preprocessed, embedded, stored, indexed, retrieved, and eventually surfaced in outputs. Each stage exposes new potential for misuse or leakage, so understanding these routes is key to assessing real risk.
Step 3: Classify and tag: Once assets and flows are identified, label them. Note data sensitivity, tenant or environment, ownership, and the business service they support. Tagging brings context, making it easier to prioritize issues and assign accountability.
Step 4: Assess controls: Evaluate the safeguards protecting each component: authentication and authorization, rate limits, output filters, content safety checks, retention policies, and audit logs. This step connects security policy to implementation and reveals where protections may be inconsistent or missing.
Step 5: Detect drift: AI systems rarely stand still. Watch for version changes, new retrieval endpoints, policy edits, or permission shifts that introduce unplanned variance. Drift detection helps teams catch changes before they become incidents.
Step 6: Monitor and respond: Finally, establish continuous monitoring that focuses on quality, not quantity. High-signal alerts, such as jailbreak attempts, anomalous tool use, or unusual retrieval patterns, should trigger a quick investigation and a coordinated response.
Together, these steps transform AI security from reactive triage into an operational discipline. Mapping the stack not only makes the invisible visible; it also reveals the underlying structure. It fosters a shared understanding among developers, security teams, and leaders of how the AI ecosystem truly operates.
Compliance and operational risk without visibility
Without visibility, even the most sophisticated AI program operates on fragile ground. When teams can’t trace the origin of data, how a model was trained, or which plugins have access to sensitive systems, both security and compliance begin to erode.
The first signs of trouble often appear during audits. Regulators and internal reviewers increasingly expect organizations to demonstrate model lineage, dataset provenance, and plugin permissions, not just to claim they exist. Without a clear map of these elements, proving compliance becomes a matter of guesswork, leaving gaps that can result in fines, failed assessments, or reputational damage.
Operational risk follows closely behind. When incident response teams lack an accurate understanding of how information flows through the system, containment becomes slower and less precise. Every undocumented data flow or untracked dependency becomes a blind corner where breaches can linger unnoticed.
Then there’s the third-party layer. Many AI-native systems rely on external models, plugins, or APIs that update automatically. Without transparency into these supply chain changes, organizations inherit risk they can’t measure or mitigate. A silent plugin update or an unverified model patch can quietly alter functionality or introduce vulnerabilities long before anyone realizes.
Visibility, in this context, isn’t just a technical advantage. It’s the difference between operating with confidence and operating on trust alone. In AI security, trust without verification is a risk few can afford.
How to get started
For teams ready to take action, the path to securing AI-native systems doesn’t have to start with a sweeping transformation. The key is to begin small but move deliberately, focusing on one high-impact workflow and proving the value of visibility.
Start by identifying a critical AI-native workflow, something central to your business, whether it’s a model serving customer interactions or an internal agent handling sensitive data. Build its AI-BOM to document every model, plugin, dataset, and policy that shapes its behavior. This single inventory becomes your baseline for understanding risk.
Next, enable continuous discovery across that workflow’s components. Track changes to models, plugins, and retrieval paths as they happen. From there, layer in drift rules to catch unplanned shifts, model version updates, new tool scopes, or policy edits that could alter behavior or permissions.
To keep this process operational, integrate visibility into existing workflows. Connect discovery tools to your ticketing or chat systems to clearly define ownership and response times. The goal isn’t more dashboards. It’s a faster, more accountable action when something changes.
Finally, define a few simple executive metrics to measure progress:
The percentage of AI assets inventoried (how much of your environment is visible)
The mean time to visibility for changes (how quickly updates are detected)
The percentage of data flows with enforced guardrails (how consistently policies are applied)
With these foundations in place, security teams can demonstrate measurable impact in a single quarter, showing that continuous visibility isn’t a future goal but a practical, scalable habit that strengthens every part of the AI lifecycle.
Unified, continuous, and autonomous security
Shadow AI can’t be solved by stitching together a collection of tools that only see a narrow slice of the system. In a scenario where one tool inspects prompts, another scans code, and another monitors runtime traffic, it leaves significant blind spots across models, agents, datasets, and infrastructure.
The result is fragmented signals that never connect into a single risk picture. Even when a team discovers Shadow AI, there’s no unified way to enforce policy or drive remediation, forcing security to manually correlate findings and maintain brittle integrations. AI systems behave as one interconnected system; security has to operate the same way. Organizations need to eliminate Shadow AI by linking inventory, policy, and remediation in one automated loop.
See what your AI is really doing
AI-native systems are evolving faster than traditional security can keep up with. Without active mapping, their attack surfaces stay fluid, opaque, and full of unknowns.
AISPM and the AI-BOM provide the structure to change, offering continuous visibility, stronger governance, and a clear path to safer, faster delivery. Want to try it out for yourself? Try Snyk AI-BOM today.
