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Adopt exposure management as a continuous, risk-based practice: inventory what is reachable, decide what must remain exposed, prioritize weaknesses in business and threat context, reduce risk, and reassess as systems change. Include AI applications, agents, integrations, models, data, and supply-chain components in the same operating picture—not just conventional servers and software.
What exposure management means when AI is in use
Exposure management is the ongoing work of finding and understanding assets that could be reached or abused, then reducing the exposures that create unacceptable risk. It is not simply a vulnerability scan or a list of software flaws. The useful outcome is a maintained view of what is exposed, why it is exposed, what depends on it, and who can act on the findings.
AI broadens that view. Alongside public-facing infrastructure, include organizational AI applications, custom agents, employee-facing tools that touch company data or systems, third-party integrations, models and components, and the data and services they rely on. NIST describes AI security concerns spanning systems, training data, outputs, software, and hardware; Gartner’s June 2, 2026 guidance identifies AI applications and their integrations as part of the expanded attack surface. NIST’s AI security and resilience overview and Gartner’s threat guidance provide further context.
Adopt it in a practical sequence
1. Assign scope and owners
Make one team accountable for coordinating the process, while giving security, IT, cloud, application, data, and AI owners responsibility for their systems. Define which environments are in scope: production and internal applications, internet-facing services, custom agents, third-party integrations, and employee-facing applications when they interact with organizational data or systems. Gartner recommends mapping these AI-related surfaces rather than treating them as outside the conventional security inventory.
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2. Build an inventory that connects assets to dependencies
Start by identifying internet-accessible assets, then connect them to software, cloud resources, identities, data, and AI systems. Record relevant models and components as well as the systems that host or connect to them. A disconnected list of IP addresses, AI tools, and vulnerabilities makes it difficult to judge business impact; linked records help show what a finding could affect and who owns the response.
CISA’s Internet Exposure Reduction Guidance names Thingful, Censys, Shodan, and Shadowserver as examples of web-based platforms for identifying internet-exposed assets. CISA notes that their capabilities differ and that their inclusion is not government endorsement. Evaluate any discovery method for coverage and fit rather than treating the named platforms as a ranked or approved shortlist. For software and AI supply-chain visibility, Gartner recommends comprehensive software inventories and calls for vendors to provide software and AI bills of materials.
3. Decide which exposures are necessary
For each exposed service, establish its operational purpose, business owner, and whether it genuinely needs public access. Remove or restrict unnecessary exposure. Before changing access, map dependencies and confirm that the change will not interrupt a critical service. An asset can be important to operations and still have an unnecessarily broad route of access.
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Do not rank work by vulnerability count alone. Consider the asset’s business importance, how it is exposed, relevant threat information, the likely impact, and the organization’s ability to remediate or contain it. This is a practical way to combine asset context, exposure, threat signals, and operational constraints; the cited guidance does not establish a universal scoring formula.
For services that must remain reachable, CISA recommends measures including changing default passwords, applying security patches, replacing unsupported products, using monitored jump hosts, monitoring ingress and egress traffic, and enabling multifactor authentication where possible. Sequence fixes with the service owner, document exceptions, and use access restrictions or other compensating controls when an immediate permanent fix is not feasible.
5. Apply AI-specific controls through development and operation
For AI applications, Gartner recommends secure development lifecycle practices, threat modeling, data classification, purpose-based access controls, and runtime monitoring. Test for prompt injection during development; combine input validation with monitoring and runtime controls rather than assuming a single filter will prevent misuse. Monitor agents and their tools at runtime, especially where an agent can act on business systems or sensitive data.
Address supply-chain exposure as part of this work. Gartner recommends software and AI bills of materials, curated repositories for third-party code, container images, and AI models, protected build systems, signed artifacts, least-privilege access, and runtime monitoring of agentic tools. These controls help organizations understand what entered a system, limit who or what can change it, and observe how it behaves in use.
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Set a repeatable assessment cadence and trigger a review when a new public service, AI deployment, integration, or infrastructure change alters the exposure picture. CISA recommends routine assessment and monitoring as IT environments evolve; continuous assessment can help surface new exposures between scheduled reviews. Record findings, owners, decisions, and remediation status so each review updates the operating picture instead of restarting discovery from scratch.
What AI changes—and what it does not
AI systems still require protection of confidentiality, integrity, and availability. Their risks also include attacks on models and data, misuse of model outputs, and weaknesses in tools or services connected to an AI application. NIST identifies issues such as evasion, model extraction, membership inference, and availability attacks, and notes that existing guidance does not yet comprehensively address these risks.
NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, published March 24, 2025, organizes terminology around machine-learning methods, lifecycle stages, and attacker goals, objectives, capabilities, and knowledge. It can help teams use consistent language when building a threat model; it is not a substitute for assessing the organization’s own applications, data, permissions, and operating context.
Gartner’s June 2, 2026 public guidance names deepfakes, AI application compromise, prompt injection, and software supply chains among critical threats for cybersecurity leaders. These areas do not all call for the same control: map the attack surface, assess the relevant threat, and apply controls appropriate to the system and the potential consequence.
Frameworks and discovery tools
NIST AI Risk Management Framework
The NIST AI Risk Management Framework is voluntary and intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST released AI RMF 1.0 on January 26, 2023, and the framework page notes that revision is underway. NIST released its Generative AI Profile on July 26, 2024. Check NIST’s page for the current status and materials before adopting a version as an organizational reference.
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Internet exposure discovery
CISA’s guidance offers a practical starting point for finding internet-exposed assets and reducing unnecessary exposure. Its named discovery platforms are examples with differing capabilities, not endorsements. Use findings as leads to validate against internal asset and ownership records; an external observation alone may not explain an asset’s purpose or dependencies.
Context-aware telemetry for CPS and OT
Gartner’s public abstract for its AI-Based Threat Exposure Management Framework to Improve CPS/OT Security says that unchecked IT integration can expand cyber-physical systems and operational technology attack surfaces, while siloed telemetry can create blind spots. It describes AI for unified, context-aware telemetry. The complete framework is not publicly available in the cited abstract, so the abstract does not support attributing detailed implementation steps to that framework.
How to evaluate exposure-management capabilities
Whether assessing existing processes or evaluating tools, use criteria tied to the operating model rather than assuming a product automatically provides complete coverage. Useful questions include:
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- Connected context: Can it bring together useful asset, identity, vulnerability, cloud, data, and threat information instead of leaving teams with isolated findings?
- Prioritization and action: Can owners validate findings, decide what matters, and track remediation or an approved restriction through existing workflows?
- Change and runtime visibility: Can assessments be repeated as environments change, and can relevant AI systems and agentic tools be monitored during operation?
- Lifecycle and supply-chain coverage: Can the organization inventory models and components, test AI systems, and connect findings to development, build, and incident-response processes?
These are selection criteria derived from CISA’s asset and assessment guidance and Gartner’s recommendations on AI applications, supply chains, and context-aware telemetry; they are not claims that one tool meets every need.
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