Many expressions describe advances in the artificial intelligence (AI) security ecosystem: leading edge, bleeding edge, over the edge. The simple truth is, though, in AI there is no “present.” If an organization is not speeding, or even stumbling, into the future, it’s already parked in the past. And if yesterday is its current space-time location, that organization is vulnerable not only to a generic, blunt-force, might-happen attack, but also to bespoke, scalpel-edged attacks that will rapidly and successfully penetrate its perimeter, infect its ecosystem, contaminate its data, and compromise its brand before the security team even realizes it’s been hit.
Some questions, therefore, that organizations must ask themselves and answer in the very near future are:
♦ What will persuade the organization to move from a reactive posture (wait for an attack and react) to a preemptive posture (harden/defend before any attack is made)?
♦ What, other than an attack, will persuade the organization that a preemptive posture is a key competitive advantage?
AI has shown itself to be a transformative social–and antisocial, in the wrong hands–force with immense potential across various industries. From natural language processing (NLP) models to computer vision and beyond, AI technologies have changed business operations at all levels. They have also changed the way security teams address critical business concerns, such as data security and data privacy. Traditional plans and practices need to be re-thought out, reconfigured, and, in some cases, retired, to ensure robust protections for new data streams and workflows.
Challenges
The proliferation of AI-driven optimization tools has led to an exponential growth in the volume of data that organizations can ingest and analyze, which, in turn, has created a gold mine of information ripe for exploitation by threat actors. The challenge is not just data loss prevention (DLP), but protecting the AI tools that are tasked with manipulating it: the models.
Some of the most common threats to models are:
♦ Adversarial Attacks, in which threat actors damage or alter inputs to ensure the model produces incorrect outputs, which can lead to potentially disastrous consequences.
♦ Model Inversion Attacks, in which the threat actors use the model’s outputs in a reverse-engineering attempt aimed at identifying the data used to train the model
♦ Model Poisoning Attacks, in which threat actors inject malicious data into the training dataset with the goal of compromising the model’s integrity and performance
Best Practices
Developing and deploying a comprehensive security approach is a critical step in safeguarding an organization’s people, processes, and property, including intellectual property (IP). Best practices include:
♦ Data Encryption: Encrypting data while in transit and at rest is one layer of protection against unauthorized access.
♦ Data Anonymization: Techniques that remove personally identifiable information (PII) from data to ensure privacy will enhance DLP efforts when data is being used or transferred.
♦ Secure Storage and Access Control: Robust access controls and secure storage protocols serve to limit data access to authorized personnel only.
♦ Regular Security Audits and Vulnerability Assessments: Regular and frequent evaluations of system activity and integrity can identify and address potential weaknesses in hardware, software, and infrastructure.
♦ Model Versioning: Maintaining a clear, accurate, and detailed history of model updates enables developers and others to track changes and ensure transparency in the event of a security breach or model failure.
♦ Model Governance: Creating a governance framework for managing models used anywhere across the enterprise can provide guidance for usage, maintenance, and compliance with data protection regulations, among other benefits.
Securing NLP Models
NLP is one of the most transformative applications of AI, enabling machines to understand and generate human language by finding relationships between language elements, such as letters, words, sentences, etc., that are present in a text dataset. Securing NLP models involves specific considerations:
♦ Model Explainability: Understanding how these models reach their conclusions can help identify potential biases and other issues. Knowing this can ensure transparency in decision-making.
♦ Continuous Monitoring: Implement continuous model monitoring to detect anomalies or unusual behavior in real time that could indicate a breach or infiltration.
♦ Secure APIs: Ensure APIs used to access the model are secure and enforce authentication and authorization mechanisms to control access.
Protecting Confidential Data
Every organization–commercial, government, for-profit, non-profit–relies on its data, and keeping proprietary, confidential data secure is the real priority for each of them, regardless of what their websites state. The best AI systems can analyze, churn, and reimagine data in seconds, but are of no value to an organization if their security solutions are porous. Incorporating strong machine learning operations (MLOps) tools as part of an overall security infrastructure can play a pivotal role in ensuring the security and reliability of AI systems by streamlining the model development, deployment, and management. Some popular MLOps tools for AI security include:
♦ TensorFlow Extended (TFX): Developed by Google, this is an end-to-end platform that enables secure deployment of production ML pipelines.
♦ Kubeflow: Built on top of Kubernetes, this solution provides tools for deploying and managing ML models at scale and offers enhanced security features.
♦ ModelDB: This is an open-source model versioning tool that tracks and manages model changes while ensuring model transparency and explainability.
♦ PySyft: A library for secure and private deep learning, this tool enables encrypted computations and federated learning.
Zero Trust Model for AI Security
The popularity of the Zero Trust Maturity Model has been increasing in the AI security ecosystem. This security approach, developed by the U.S. Cybersecurity and Infrastructure Security Agency (CISA) is based on the principle of never trust, always verify. This means no entity within or outside the network is trusted by default and every access request must be thoroughly verified before access to resources is granted. This security model can scale to any organization by ensuring the following steps are taken and adhered to:
♦ Identity and Access Management (IAM): Implement strict IAM policies to control access to AI models and data.
♦ Continuous Monitoring: Employ continuous monitoring and anomaly detection mechanisms to detect unauthorized activities promptly.
♦ Authentication and Authorization: Enforce multi-factor authentication and fine-grained authorization controls to prevent unauthorized access.
♦ Network Segmentation: Segment the AI infrastructure into small, isolated networks, reducing the attack surface in case of a breach.
AI, with all of its benefits and risks, is here to stay and will continue to shape the way we live, work, and play. Ensuring data protection and AI security must remain a top priority for everyone, but especially for the organizations that are leveraging this powerful technology in ways that affect other organizations and individuals. These organizations can–and should–fully harness the potential of AI and mitigate the associated risks by embracing a security-first, security-always approach.
