12 Days of AI Risk Management: Days 9-12

Learn to detect AI attacks, develop ‘explainable’ AI and make your action plan ‘non-negotiable’

By Jacqueline Lebo

SAFE presents the final installment of a 12-part action plan to get your organization out in front of the #1 cyber risk management challenge of the coming year, AI risk. Take it one “day” at a time, face 2026 with confidence.

Day 9: Continuous Monitoring: Detecting AI-Powered Attacks

Get ready for low-signal, highly targeted maneuvers that render traditional signature-based monitoring obsolete.

Day 10: The ‘Explainability Mandate’ (XAI) in Security

Without explainable AI, an organization cannot satisfy regulatory, legal, or audit inquiries into a model’s decision-making. 

Day 11: Budgeting for AI Risk Where to Invest in 2026

The key to securing budget is demonstrating that new investment directly addresses unquantified financial risk.

Day 12: The AI Risk Playbook: Your First 90 Days

Achieve governance velocity by focusing on control transparency and executive accountability, directly addressing the core risks identified in this series.

Learn about AI risk management with SAFE

Day 9: Continuous Monitoring: Detecting AI-Powered Attacks

The arms race in cybersecurity is rapidly escalating. Adversarial adoption of AI is moving beyond simple scripting to the deployment of sophisticated, hyper-customized, and machine-speed attacks. The next generation of breaches will not be characterized by loud, large-volume traffic, but by low-signal, highly targeted maneuvers executed with perfect timing—a challenge that renders traditional signature-based monitoring obsolete.

Strategic Insight: Identifying Telltale Patterns of AI Adversaries

Effective continuous monitoring must evolve to detect the behavioral hallmarks of an AI-driven attacker. AI excels at achieving precision and persistence at a scale impossible for human threat actors. Identifying these telltale patterns requires correlation across multiple control layers.

Key AI attack indicators include:

• Hyper-Customization: AI enables phishing and social engineering campaigns to be personalized based on deep background research (scraping social media, public records). The attack signature is no longer a mass-sent, generic email but a perfectly crafted, one-off message referencing specific, niche internal projects or relationships.
• Machine-Speed Reconnaissance: AI attackers can perform vulnerability scanning, credential stuffing, and open-source intelligence (OSINT) gathering simultaneously and non-stop, mapping attack surfaces faster than human defenders can patch or configure them.
• Low-Signal Lateral Movement: Instead of generating many failed logins or broad network sweeps, AI identifies the minimal, lowest-privilege path needed to move between systems, using legitimate credentials and generating minimal noise that often slips below human alert thresholds.

Leveraging AI for Third-Party Continuous Monitoring

The challenge of detecting AI-powered attacks is compounded when the attack originates or pivots through the supply chain. This is where continuous third-party monitoring becomes critical.

SAFE’s approach to Third-Party Risk Management (TPRM) provides an essential capability for this defense:

• External Posture Analysis: SAFE continuously monitors the external hygiene of vendors, not just through outside-in scanning but by applying AI to analyze thousands of data points related to the vendor’s configuration, patch status, and public exposure.
• Anomaly Detection: The AI model establishes a baseline of “normal” security hygiene for the vendor. If a vendor suddenly and drastically increases its exposed attack surface (e.g., rapid deployment of unsecure services, sudden failure to patch), the AI flags this abnormal change in behavior.
• Pre-Attack Warning: This anomaly often represents the necessary precursor for an AI-powered attack to take root. By detecting the deterioration of the security control effectiveness at the third party—the target environment that an AI adversary is exploiting—the host organization receives a critical warning before the attack campaign begins.

This continuous, behavioral approach shifts the defense from detecting the attack to detecting the preconditions for the attack, dramatically reducing the Threat Event Frequency (TEF) originating from the supply chain.

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Deliverable and Clear Actionable Next Steps

Continuous monitoring must now look for subtle, machine-driven indicators of compromise.

Checklist of 5 New “AI Attack Indicators” to Add to Current SIEM Alerts:

1. Velocity of System Probing: Alert when a single IP or user account attempts multi-vector probing (e.g., DNS queries, port scans, and HTTP requests) on five or more distinct internal systems within a 30-second window. (Indicates machine-speed recon/lateral movement).
2. Credential Cycling/Switching: Alert when a user account utilizes more than three distinct, successful credential sets (e.g., password, API key, service ticket) within a 5-minute period. (Indicates AI optimizing lateral movement).
3. Low-Entropy Outbound Traffic: Alert on outbound connections containing unusually low-entropy data over standard protocols (HTTPS/DNS), indicating encrypted C2 communication designed to evade signature detection.
4. Phishing URL Uniqueness: Use AI to flag incoming phishing URLs that are unique (not seen globally) but display high grammatical complexity and perfect contextual relevance. (Indicates hyper-customization).
5. Rapid Third-Party Control Degradation: Alert immediately when a Tier 1 vendor’s security posture score drops by 15% or more within 24 hours, as quantified by continuous monitoring tools. (Indicates environmental prep for exploitation).

Next Steps for Risk Leaders:

1. Update Threat Models: Update threat modeling sessions to include “AI as Adversary” scenarios, specifically focusing on low-signal lateral movement and social engineering.
2. Enhance Detection Logic: Task security operations with implementing the five new AI Attack Indicators in the SIEM/SOAR system, moving beyond simple volumetric alerts.
3. Integrate Third-Party Data: Ensure that continuous third-party risk data is directly integrated into the security operations center (SOC) feed, making third-party degradation a Tier 1 security alert.

Day 10: The ‘Explainability’ Mandate (XAI) in Security

The modern enterprise relies on AI models to make critical security decisions—from granting privileged access and flagging insider threats to blocking network traffic. When these decisions are made by a deep learning model without human-readable rationale (the “Black Box”), a fundamental flaw in accountability is introduced. In cybersecurity, a decision that cannot be explained cannot be defended, audited, or ultimately trusted.

Strategic Insight: XAI as the Foundation of Accountability

The pursuit of eXplainable AI (XAI) is not a technical preference; it is a risk mandate. From a FAIR-AI Risk (FAIR-AIR) perspective, the lack of XAI fundamentally compromises the organization’s ability to manage Model Loss Magnitude (ML) and Model Loss Event Frequency (LEF).

• Auditability and Loss Magnitude: Without XAI, the organization cannot satisfy regulatory, legal, or audit inquiries into a model’s decision-making. If an AI system mistakenly blocks a legitimate business transaction or denies a customer service due to bias (a high-magnitude loss event), the organization cannot provide a traceable root cause. This failure to demonstrate due diligence amplifies regulatory fines and litigation costs.
• Trust and Control Strength: XAI allows security professionals to validate the model’s logic. If the model flags a critical server compromise because of a bizarre, non-security-related input (e.g., the time of day), XAI exposes this flawed logic. This allows analysts to correct the model or data input, thus strengthening Model Control Effectiveness and reducing the Model Loss Event Frequency.

XAI moves the organization past blind reliance on accuracy metrics. It mandates transparency, allowing auditors, executives, and legal counsel to understand why an action was taken, restoring human accountability over machine decision-making.

XAI in Action: Audit-Ready Traceability with SAFE

XAI principles require that every security-critical AI decision—whether it’s an anomaly detection system flagging a user for insider threat or a system blocking access to an API—must be accompanied by a clear, human-intelligible explanation of the feature importance and contributing factors that led to the outcome.

The SAFE One platform provides a prime example of operationalizing XAI for risk quantification by ensuring every data point feeding its AI-driven CRQ model has clear, granular traceability back to its original source. This eliminates the “black box” and provides audit-ready evidence:

• Contract Analyzer Agent: When the platform identifies a missing data breach notification SLA for a third-party vendor, the finding is directly linked to the exact PDF page, paragraph, and clause of the executed vendor contract where the inadequacy was identified. This directly informs the Legal Loss Magnitude (ML) exposure related to potential regulatory fines from delayed disclosure.
• Outside-In Scanning Agent: If the platform flags a high-risk vulnerability (e.g., an unpatched Apache Tomcat version on port 8080), the finding is traceable to:
  • The specific IP address and DNS record that was scanned.
  • The precise timestamp of the scan and the Scanner ID.
  • The specific version number reported by the server banner/header that triggered the vulnerability rule (CVE-2023-XXXXX). This directly increases the Threat Event Frequency (TEF) and Vulnerability score for the affected asset, raising the financial Annualized Loss Exposure (ALE).

In both instances, the AI-driven assessment is not an opaque declaration. It is a demonstrable conclusion, allowing auditors and leaders to understand precisely what was detected, where it came from, and how it impacts the quantified risk. This direct linkage ensures the system is inherently Explainable and Audit-Ready, satisfying the non-negotiable XAI and governance standards.

Day 11: Budgeting for AI Risk: Where to Invest in 2026

As AI shifts from an experimental technology to a core enterprise utility, so too must the security and risk budget shift. Simply allocating funds to existing cyber controls will not cover the unique, systemic risks introduced by AI. The key to securing budget approval is demonstrating that new investment directly addresses unquantified financial risk, moving past technical fear to data-backed justification.

Strategic Insight: Investment Focused on Unquantified Loss

Security and risk budgets for AI must be focused on closing the largest gaps in Model Loss Event Frequency (LEF) and ensuring the lowest possible Model Loss Magnitude (ML), as defined by the FAIR-AI Risk (FAIR-AIR) framework.

FAIR-AIR as the Budget Accelerator:

The FAIR-AIR model provides the precise language needed to justify these new investments to the Board:

• The Problem State (High ALE): Currently, the organization faces a high and largely unmanaged Annualized Loss Exposure (ALE) because:
  • Loss Event Frequency (LEF) is High: Shadow AI and the lack of continuous model validation mean that control failures and policy breaches (the loss events) are highly probable and unpredictable.
  • Loss Magnitude (ML) is Maximum: Lack of governance, especially regarding sensitive data input and accountability (XAI), ensures that if a loss event occurs, the resulting financial impact (regulatory fines, litigation, reputation damage) will be catastrophic.
• The Investment Goal (Reduced ALE): Investment in the three critical areas below directly reduces the risk components, resulting in a quantified reduction in ALE:

1. AI Model Validation Tooling: (Directly Reduces Model LEF) This investment strengthens Model Control Effectiveness, reducing the probability of model failure (the loss event).
2. Centralized AI Governance Platforms: (Directly Manages Model ML) This investment enforces policy, limiting the scope of potential breaches and thus reducing the financial severity of any single loss event.
3. Upskilling/Training for Risk and Security Teams: (Reduces Uncertainty) This investment allows the human team to validate the model and its controls, reducing the Uncertainty Factor in the overall FAIR calculation, providing higher confidence in the low-risk score.

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Deliverable and Clear Actionable Next Steps

Budgetary requests must be framed not as security costs, but as necessary investments to contain quantifiable financial risk.

Justification Slide for the Board: “Three Critical AI Risk Investment Areas for the Next Fiscal Year”

Investment Area	Problem Statement (Unmanaged Risk)	Financial Impact (FAIR-AIR Benefit)	Expected ROI
1. AI Model Validation Tooling	Model Degradation: Unmonitored models introduce unknown vulnerabilities and increase Model Loss Event Frequency (LEF) over time.	LEF Reduction: Continuous validation reduces the probability of a model failure (the loss event) by proving Model Control Effectiveness remains high.	Cost: $250K ALE Reduction:$1.25M ROI: 400%
2. Centralized AI Governance Platforms	Shadow AI & Policy Failure: Decentralized models allow high-value data exposure, leading to maximum Model Loss Magnitude (ML) when a breach occurs.	ML Reduction: Centralized control limits data exposure and enforces policy, shrinking the financial severity of regulatory fines and legal costs.	Cost: $400K ALE Reduction:$2.4M ROI: 500%
3. Upskilling/Training (Risk & Audit)	Control Uncertainty: Lack of AI literacy prevents effective audit, creating an unacceptable Uncertainty Factor in the overall risk calculation.	Reduced Uncertainty: Enables human teams to audit XAI outputs, stabilizing the Model Control Strength factor, providing high-confidence risk assurance.	Cost: $50K ALE Reduction:$200K ROI: 300%

Note: The “ALE Reduction” represents the reduction in the calculated Annualized Loss Exposure due to the increased control effectiveness and decreased uncertainty achieved by the investment.

Next Steps for Risk Leaders:

1. Quantify Current Risk: Use your CRQ platform (like SAFE) to calculate the current Annualized Loss Exposure (ALE) associated with your highest-risk AI projects without these investments (high uncertainty).
2. Model Investment ROI: Utilize the FAIR-AIR methodology to precisely calculate the expected reduction in ALE (Risk Reduction) that these three investments would provide, using the Avoided Loss metric to justify the budget request.
3. Finalize Vendor Selection: Identify leading solutions that can fulfill the requirements of all three investment areas, focusing on tools that provide quantifiable assurance and FAIR-AIR alignment, moving the conversation from if to which.

Day 12: The AI Risk Playbook: Your First 90 Days

The preceding eleven days have established that AI risk is not a futuristic problem; it is a current, systemic financial and governance liability. Every day without a coherent governance structure exposes the enterprise to unmanaged Loss Event Frequency (LEF) and catastrophic Loss Magnitude (ML).

The solution is an immediate, structured response. This final post serves as the preview fo ra comprehensive “AI Risk Playbook” (coming soon)  offering a high-level, three-step action plan designed to lay a foundational governance structure in the first 90 days. The goal is to move the organization from reacting to AI risk to quantifying and controlling it.

Strategic Insight: Governance Velocity and NIST Alignment

The objective of the first 90 days is to achieve governance velocity by focusing on control transparency and executive accountability, directly addressing the core risks identified in this series (Shadow AI, GenAI policy failures, XAI requirements).

This action plan is not arbitrary; it is a rapid deployment roadmap for implementing the foundational GOVERN, MAP, MEASURE, and MANAGE Functions of the NIST AI Risk Management Framework (AI RMF), providing immediate executive assurance and audit readiness.

Phase	Duration	Primary Objective	NIST RMF Alignment	FAIR-AIR Impact
Phase I: Inventory	Days 1–30	Discover and classify all AI usage (Shadow AI) to transition from unknown risk to known, manageable liability.	MAP (Contextualizing AI Risk)	Reduces Uncertainty and sets the boundary for Model Loss Event Frequency (LEF).
Phase II: Policy	Days 31–60	Implement and enforce minimum security and ethical standards for all GenAI use cases.	GOVERN (Establishing Policy & Accountability)	Limits Loss Magnitude (ML)by restricting the input of sensitive data.
Phase III: Accountability	Days 61–90	Formalize executive ownership and mandate continuous risk quantification (CRQ) for AI models.	MEASURE & MANAGE(Quantification and Control)	Strengthens Control Effectiveness and drives a measurable reduction in ALE.

Deliverable: The 3-Step, 90-Day AI Risk Foundation Plan

This is the non-negotiable action plan required to secure your organization’s AI future.

Step 1: Inventory the Unknown (Days 1–30)

(NIST AI RMF: MAP Function)

• Action: Launch a dedicated technical initiative to use Network Traffic Analysis and automated code scanning to map the complete footprint of Shadow AI and sanctioned models (as discussed in Day 4).
• Focus: Determine what data (e.g., PHI, IP) is being input into the models to quantify the worst-case Loss Magnitude (ML) exposure.
• Result: A fully classified AI Asset Register, detailing model location, owner, and the data sensitivity, which satisfies the NIST RMF’s AI-CM (AI System Characteristics) requirements.

Step 2: Enforce the Boundary (Days 31–60)

(NIST AI RMF: GOVERN Function)

• Action: Formally adopt and disseminate the mandatory GenAI Usage Policy (as detailed in Day 7), enforcing the three non-negotiable clauses: Sensitive Data Prohibition, Human Verification, and Approved Tools List.
• Focus: Implement technical controls to enforce the policy, ensuring Risk-Based Policy Enforcement where high-risk use cases are confined to controlled environments.
• Result: Compliance with NIST RMF’s AI-GC (AI Governance) and AI-AC (AI Actor Accountability) by defining roles and policy limits, thereby placing a ceiling on potential legal and financial losses.

Step 3: Formalize Accountability (Days 61–90)

(NIST AI RMF: MEASURE & MANAGE Functions)

• Action: Present the newly quantified AI risk (ALE) based on the Inventory to the Board. Mandate the establishment of a formal Model Risk Management (MRM) function.
• Focus: Establish the requirement for continuous risk analysis using the FAIR-AIR methodology. This includes mandating XAI (Day 10) and continuous Model Validation (Day 6) to monitor control effectiveness.
• Result: A Board-approved AI Risk Charter that mandates the integration of AI risk into the financial CRQ process, fulfilling the NIST RMF’s AI-ME (Measurement) and AI-RM (Monitoring) requirements. This final step drives a measurable reduction in your organization’s financial exposure.

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Conclusion

The 12-day series has provided the why, the how, and the what of AI risk management. See the first and second parts of the series:

Days 1-4 of AI Risk Management

Days 5-8 of AI Risk Management

Next, it’s time for the roadmap. Watch this space to see the full playbook – coming soon!

Learn more about SAFE’s capabilities in AI Risk Management.