The AiM FRAME™ Maturity Levels

The AiM FRAME™ is structured around five distinct levels of maturity. Each level reflects a step in an organization’s journey from reactive operations to autonomous resilience. This framework is not just a scale—it’s a strategic map to align culture, governance, and technology.

1. Reactive

• Core Characteristics:
  OT systems operate in isolation or silos with little to no cybersecurity integration. Responses to threats are manual and event-driven. AI is not present or is viewed as a future consideration.
• Key Capabilities:
  Minimal monitoring. No AI planning. Reactive incident response. Inconsistent data collection.
• Governance Expectations:
  Ad hoc decision-making. No formal AI policies or OT/IT coordination.
• Assurance Strategies:
  Basic risk acknowledgment. No measurable confidence in system behavior or outcomes.
• Example:
  A facility responding to cyber incidents only after service outages—without understanding root causes.

2. Informed

• Core Characteristics:
  Awareness of the value of AI and data in OT systems begins to grow. The organization starts tracking cyber incidents and analyzing vulnerabilities.
• Key Capabilities:
  Foundational inventory of OT assets. Data pipelines begin forming. Pilot monitoring systems may be introduced.
• Governance Expectations:
  Cyber roles and responsibilities are defined. AI use is considered in risk discussions.
• Assurance Strategies:
  Documentation of known risks. Baseline metrics begin to form.
• Example:
  A water utility logs SCADA anomalies and evaluates AI vendors for future predictive tools.

3. Integrated

• Core Characteristics:
  Cybersecurity is integrated into operations. AI-enabled tools are deployed for anomaly detection, performance analysis, or planning. OT and IT coordination improves.
• Key Capabilities:
  Data is centralized for analysis. AI models assist human operators. Incident response is guided by insights.
• Governance Expectations:
  Policies for AI procurement, monitoring, and auditing are in place. Cross-functional governance bodies exist.
• Assurance Strategies:
  Internal audits of AI-driven systems. Change control processes include AI oversight.
• Example:
  A rail transit agency uses AI to detect early signs of equipment failure and coordinates actions across engineering and cybersecurity.

4. Predictive

• Core Characteristics:
  The organization anticipates issues using AI-driven models trained on historical and real-time data. It moves from reaction to prevention.
• Key Capabilities:
  Real-time data fusion. Predictive maintenance. Threat forecasting.
• Governance Expectations:
  Risk committees review AI performance. Ethical and bias checks are routine.
• Assurance Strategies:
  AI validation frameworks. Scenario testing. Explainability requirements are enforced.
• Example:
  An energy provider uses AI to simulate load balancing and predict cyber-physical interactions under stress conditions.

5. Autonomous

• Core Characteristics:
  AI is trusted to make and implement decisions within defined guardrails. Systems self-heal, reroute, or adjust in real time.
• Key Capabilities:
  Automated incident response. Autonomous operational optimization. Continuous learning.
• Governance Expectations:
  Governance-by-design principles. Continuous stakeholder review. Regulatory alignment.
• Assurance Strategies:
  Continuous validation. Red-team simulations. Real-time assurance dashboards.
• Example:
  A smart grid that autonomously mitigates supply threats, reconfigures nodes, and sends risk alerts to leadership in real-time.