Abstract

MIT's AI Risk Repository provides the first systematic compilation of AI-related risks, cataloguing 1,700+ risks extracted from 74 frameworks across 7 domains and 2 taxonomies (Causal and Domain).

This opinion translates MIT's academic framework into actionable risk management for financial services leaders navigating AI adoption under GDPR, AI Act, and banking regulations.

Key takeaway: Without proactive risk mapping, AI deployment in regulated environments increases compliance exposure and operational fragility.

Key findings

• Seven risk domains cover the AI threat landscape

  • MIT's Domain Taxonomy classifies AI risks into seven categories, each containing 2-4 subdomains totaling 24 specific risk areas.

    • (1) Discrimination & Toxicity (1.1 unfair treatment, 1.2 toxic content exposure, 1.3 unequal performance), (2) Privacy & Security (2.1 data compromise, 2.2 system vulnerabilities, 2.3 unauthorized access), (3) Misinformation (3.1 false information generation, 3.2 consensus reality erosion), (4) Malicious Actors (4.1 disinformation campaigns, 4.2 fraud, 4.3 cyberattacks, 4.4 weapons development), (5) Human-Computer Interaction (5.1 overreliance, 5.2 loss of human agency and autonomy), (6) Socioeconomic & Environmental (6.1 power centralization, 6.2 employment quality decline, 6.3 governance failure), (7) AI System Safety (7.1 goal misalignment, 7.2 dangerous capabilities, 7.3 lack of robustness).

  • For financial services specifically, (1) Discrimination & Toxicity, (2) Privacy & Security, (5) Human-Computer Interaction and (6) Socioeconomic & Environmental present the highest regulatory and operational exposure, directly intersecting with existing compliance frameworks (GDPR, AI Act, Basel III/IV, MiFID II).

• Top 5 critical risks for Financial Services

  • Filtering MIT's 1,700+ risks through a financial services lens reveals five priority areas requiring immediate governance:

    • Unfair Discrimination (subdomain 1.1 unfair treatment) : Algorithmic bias in credit scoring, loan approvals, or insurance pricing creates GDPR Article 22 violations (automated decision-making) and potential regulatory sanctions. Historical data reflecting past discrimination becomes embedded in AI models.

    • Privacy Compromise (subdomain 2.1 data compromise) : AI systems that memorize and leak customer data violate GDPR Article 32 (security of processing) and trigger mandatory breach notification. Example: Chatbots trained on client conversations inadvertently exposing PII in responses.

    • Misinformation Generation (subdomain 3.1 false information generation) : AI-generated financial advice or market analysis containing factual errors exposes firms to MiFID II suitability violations and client detriment claims.

    • Overreliance and Unsafe Use (subdomain 5.1 overreliance) : Delegating risk assessment to black-box AI models without human oversight contradicts prudential regulation requirements for explainability and accountability (Basel Committee principles for operational resilience).

    • Governance Failure (subdomain 6.3 governance failure) : Inadequate AI risk frameworks fail to meet AI Act requirements for high-risk system governance (Article 9: risk management systems) and internal audit trails demanded by financial regulators.

• Compliance mapping across regulatory frameworks

  • MIT's Causal Taxonomy (Entity, Intent, Timing) enables precise mapping of AI risks to regulatory obligations:

    • GDPR Intersection:

      • Pre-deployment risks: Data governance design (Article 25: privacy by design)

      • Post-deployment risks: Ongoing monitoring for discrimination (Article 5: fairness principle)

    • AI Act Intersection:

      • High-risk systems (Annex III): Credit scoring and insurance risk assessment require conformity assessment

      • Transparency obligations (Article 13): Users must be informed when interacting with AI systems

    • Banking Regulations (Basel, EBA):

      • Operational resilience: AI system robustness aligns with DORA requirements

      • Model risk management: Third-party AI vendors require enhanced due diligence

• The gap: from academic taxonomy to operational tool.

  • The problem lies in the fact that MIT offers a classification, but no implementation.

  • Financial sector leaders must translate this framework into audit protocols, risk assessment systems, and compliance processes; a task that remains to be done.

My Take

The MIT framework is a starting point, not a solution. The strategic question to ask, therefore, is this: how do we translate 1,700 academic risk descriptions into AI governance?

To move the MIT framework from an academic taxonomy to an operational tool, we could consider three actions for financial sector leaders:

• Conduct AI risk inventory.

  • Audit existing and planned AI systems against MIT's 7 domains and 24 subdomains.

  • Prioritize high-risk applications (credit decisions, fraud detection, customer-facing systems) for immediate compliance review.

• Map regulatory obligations.

  • Cross-reference identified AI risks with GDPR, AI Act, and sector-specific regulations.

  • Establish clear internal ownership: Legal (compliance requirements), Risk (mitigation strategies), Technology (implementation controls).

• Establish Cross-functional AI governance. 

  • Create AI Risk Committee with representation from Product, Legal, Risk, Technology and Compliance.

  • Use MIT's Causal Taxonomy (Pre-deployment vs. Post-deployment timing) to structure review processes at design phase and ongoing monitoring.

The strategic imperative: AI adoption without systematic risk management concentrates compliance exposure. Financial services leaders who translate MIT's academic framework into operational governance gain competitive advantage through faster, lower-risk AI deployment.

Report and Dataset: Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2024). "The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence".

• Full Report: The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence - MIT AI Risk Initiative, 2024

• Interactive Database: Explore MIT's AI Risk Database - 1,700+ risks, filterable by domain and causal factors