AI Risk Fundemantals

Understanding Emerging Technology Threats

Why AI is Different

Deterministic vs Probalistic Behaviour

  • Tradational IT is predictable, has identical outputs everytime
  • AI is statistical likelihood, varying outputs

Static vs Evolving Vulnarabilities

  • Traditional IT has fixed risks until code changes
  • AI has dynamic risks that emerge post-deployment

Clear vs Distributed Accountability

  • IT has clear responisibilty chains
  • AI makes autonomous decisions, has unclear ownership

Transperent vs Opaque Decision-Making

  • IT has Auditable source code
  • AI has billions of parameters, fundemantally unexplainable

Controlled vs Consinuous Change

  • IT has a formal change control processesm
  • AI has continuous behaviour modification from new data

Key Risks of AI Technology Categories

Large Language Models (LLMs)

Key Risks: Hallucinations, prompt injections,bias, data exposure.

Training data dependencies create consent challanges. Many LLMs train on web-scraped content without content creater authorization.

Example issues

  • An attorney used an LLM for a case, LLM generated fabricated case citations, fake quotes, ChatGPT falsely assuered the attorney, court imposed sanctions
  • Samsung used ChatGPT for code review, proprietry code and internal notes were exposed, tradational DLP systems failed to detect AI-related exposure, company banned ChatGPT and implemented AI governance controls.

Tradational Machine Learning (ML) Systems

Key Risks: Accountability gaps, process failures at scale. Model drift, algorithmic bias, discrimination.

Multimodal Systems:

Compound vulnarabilities, complex failure modes.

AI Threat Vectors

  • Prompt injection attacks targeting decision-making processes
  • Model theft and IP extraction through systematic querying
  • Training data poisoning introducing malicious content
  • AI-Powered cyberattacks with automated vulnerablity discovery
  • Insider data exposure through legitamete access explotation

Emerging Threats

Multimodal and Agentic AI
  • Cross-Modal Attack Vectors
    • Exploits combining multiple data types
    • Multimodal models 18-40x more vulnerable
  • Autonomous Decision-Making and Agentic Risks
    • Systems independently plan, execute and adapt strategies
    • Decision execute before humans can intervene
Speed and Arms Race
  • Threat Accelaration at Machine Speed
    • AI attacks operating faster than human response
    • Systems destroyed in minutes
  • Adversarial AI Arms Race
    • Attackers using AI faster than defenders
    • AI-enhanced tools test and adapt in real time
    • Agentic AI: Goal seeking adversarial agents operate autonomously

Basic AI Risk Management Readiness

Consider which AI risk management component your organization needs most urgently…

  • Technical security controls and monitoring capabilities
  • Governance policies and oversight structures
  • Staff training and awareness programs
  • Vendor evalutation and contract terms
  • Regulatory complience documentation
  • Executive leadership buy-in and budget

AI Risk Assesment Framework

  • Privacy Personal information use and data consent
  • Transarency AI system disclosure requirements
  • Accountability Clear responsibility chains for decisions
  • Fairness Demographic impact assesment
  • Sustainability Environmental cost evaluation
  • Implementation Systematic methodology with enforcement

Regulatory, Framework & Legal Landscape

  • EU AI Act complience requirements and prohibitions
  • NIST AI Risk Management Framework adoption
  • Emerging global and state-level regulations
  • Industry-specific guidance: FDA medical AI, FINRA financial services
  • Regulatory gaps enable continued violations

AI Security Vulnerabilities and Detections

  • Threat-driven vulnerabalities analysis by actor type ,
  • AI-specific exploitation techniques and attach vectors
  • Behavioral analytics for AI systems
  • Model integrity monitoring requirements
  • API vulnerabilities enable sophisticated cybercrime
  • Defense requires machine-speed autameted response

Vendor Evaluation and “Magic Box” Problem

  • Engineering infrasutruce and production track record assesment
  • Security certifications and data handling practices
  • Contract terms and liability considerations
  • Red flags: “Too complex to explain” claims
  • Focus on demostrated production success

Communicating AI Risk to Stakeholders

  • Executive Communication: Business impact and capital risk
  • Technical Teams: Implementation details and frameworks
  • End Users: Practical guidance and examples
  • Regulators : Systamatic compliance documentation

Building AI Risk Culture

  • Leadership commitment and infrastructure investment
  • Cross-functional AI risk awareness
  • Continuous learning and adaptation requirements
  • Risk-aware “innovation culture” development
  • International capability building over vendor dependence

Indistury-Specific Considerations

  • Financial Services: Model risk management and fair lending complience
  • Healthcare: Patient safety and HIPAA compliance requirements
  • Government: Security clearances and classified information protection
  • Legal Profession: Evidence authenticty and ethical billing disclosure
  • Universal Challange: Ethics as perfomative complience

Common Implementation Pitfalls

  • Treating AI risk management as one-time project
  • Underestimating cultural change required
  • Focusing only on external AI whike ignoring shadow AI. Shadow AI refers to the use of free or paid artificial intelligence tools by employees without the knowledge or approval of their employer.
  • Assuming vendors will manage all risks

30-Day Action Plan

  • Week 1
    • Discovery - AI Tech Inventory
  • Week 2
    • Engage stakeholders & Understand AI use cases
  • Week 3
    • Update the risk register and determine risk response
  • Week 4
    • Initial Risk Assesment w/Systamatic Frameworks