Securing the Future: Strategies for Adversarial Machine Learning Risk Management

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Introduction to AML Risk

The rise of Adversarial Machine Learning (AML) has transformed cybersecurity, emerging as a potent tool in espionage and warfare. High-profile cases, such as the manipulation of airport facial recognition systems and spam filter breaches, highlight AML’s potential for extreme disruption.

However, AML can be combatted, and the right defences and collaborations can allow an organisation to embrace AI without putting itself at an increased risk from AML attacks.

What is Adversarial Machine Learning?

In basic terms, AML exploits the vulnerabilities in machine learning models by focusing on adversarial inputs that prompt errors. It requires that the attackers understand the AI system, and from there introduce data to manipulate its behaviour. This is of particular concern for sectors where highly sensitive data is tied up in the AI systems, such as finance, healthcare, and national security.

Threats of Adversarial Machine Learning

Of the threats to machine learning, the most critical four to defend against are:

  • Data Poisoning: Compromised training data causes models to learn inaccuracies, leading to operational errors.

  • Evasion Attacks: Attackers craft inputs to evade detection, leading to false negatives once the model is live.

  • Model Stealing: Adversaries could replicate models using their outputs, circumventing security.

  • Model Inversion: Sensitive training data could be inferred from model outputs by attackers.

The Impact on Everyday Life

Should an AML attack be successful, there are several possible outcomes. These can come with a significant cost to the business, cause reputational damage, and potentially expose customers to data theft.

  • Fraudulent Transactions: AML techniques could be used to deceive fraud detection systems in banks, allowing malicious actors to make unauthorised transactions without detection.

  • Stock Market Manipulation: AI models that predict stock trends could be misled by adversarial manipulated data, potentially causing financial losses for investors, and destabilising the stock market.

  • Misdiagnosis: In healthcare, AML could lead to the manipulation of medical imaging AI, causing incorrect diagnoses and affecting patient treatment plans.

  • Insurance Fraud: AI systems designed to process insurance claims could be tricked into approving fraudulent claims, leading to financial losses and higher premiums for customers.

  • Surveillance Systems: Adversarial attacks could disrupt facial recognition systems used in national security, allowing individuals to avoid detection.

  • Autonomous Weapons: AML has the potential to impact autonomous defence systems, potentially leading to unintended targets being engaged or enemies being misclassified.

  • Data Privacy: Adversarial attacks could be used to infer private information from anonymised datasets, compromising individuals’ privacy.

  • Information Filtering: Manipulation of algorithms that filter news and information could lead to the spread of misinformation, affecting public opinion and democratic processes.

  • Autonomous Vehicles: Adversarial inputs could confuse AI systems in self-driving cars, possibly leading to accidents or disruptions in traffic systems.

  • Smart Home Devices: AML attacks could interfere with voice-activated AI in smart homes, leading to unauthorized actions or access to personal data.

  • Evidence Tampering: AI used to process and verify legal documents or evidence could be targeted, leading to wrongful convictions or the dismissal of valid cases.

Defending AI Against Adversarial Attacks

Defending against AML requires a multi-faceted approach. It’s important to understand that no single technology can do this. Rather, the organisation needs to build a consolidated strategy, and combine a suite of technology solutions with human oversight and management.

The eight-step approach to holistic defence that we recommend involves:

  1. Adversarial Training: Train models with adversarial examples to improve attack resilience.

  1. Input Sanitization: Filter and validate data to eliminate malicious inputs before processing.

  1. Model Hardening: Utilise techniques like feature squeezing and defensive distillation to increase model robustness.

  1. Proactive Monitoring: Continuously watch over system operations to detect and react to anomalies swiftly.

  1. Frequent Retraining: Update models with fresh data and learnings from new adversarial tactics.

  1. System Redundancy: Deploy multiple models to provide backup and validation, ensuring output integrity.

  1. Incident Protocols: Have clear, immediate action plans for potential breaches to minimise impact.

  1. Simulations and Drills: Regularly test defences with simulated attacks to refine response strategies.

AML Risk Assessment for Business and Government

By working with Excite Cyber, you can develop a consolidated and multi-faceted approach to AML risk assessment and, from there, develop a complete strategy for defence. Here is a basic list of prioritised considerations we work systematically through with our customers when conducting an AML risk assessment:

  1. AI System Types Analysis: Identify whether AI systems are predictive or generative, and pinpoint their unique vulnerabilities.

  2. Lifecycle Stage Assessment: Secure AI models during training and deployment, watching for data poisoning and evasion attacks.

  3. Identifying Attacker Goals and Objectives: Recognise adversarial intentions, whether to disrupt, compromise, or intrude.

  4. Assessing Attacker Capabilities: Evaluate adversaries’ data manipulation skills and resourcefulness.

  5. Knowledge Level of Attackers: Assess system vulnerability against attackers’ varying levels of insight.

  6. Risk Evaluation per AI Application: Execute risk evaluations tailored to the specific contexts of AI applications.

  7. Implementation of Mitigation Strategies: Develop strategies to enhance data security, including regular security audits.

  8. Ethical and Legal Implications of AML: Consider the ethical and legal ramifications, particularly regarding biased outcomes.

  9. Building Defences and Response Readiness: Establish solid defence mechanisms and emergency plans, ensuring compliance and effective communication.

  10. Embracing Continuous Risk Management: Stay proactive with continuous education and adaptation to new threats, integrating ethical and legal considerations into your defence strategy.

Protect Your Artificial Assets with ExciteCyber

Defending against AML doesn’t mean locking down the data environment or forgoing the use of AI. Doing so would put your business at a competitive disadvantage. Instead, the goal needs to be to use cyber security as a strategic opportunity towards enabling AI outcomes. By leading the charge in AML risk evaluation, Excite Cyber is equipped to fortify both commercial and governmental entities against advanced AI-related security challenges without impacting on their ability to leverage the benefits of AI.

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