Articles Posted in Cybersecurity

Introduction: AI Security Is the New Frontier

Artificial intelligence systems are no longer experimental and are embedded in financial fraud detection, autonomous vehicles, medical diagnostics, and critical infrastructure. Yet, AI security has lagged behind adoption. Hackers now target machine learning models directly, exploiting weaknesses unfamiliar to traditional IT teams. This article explains the top AI attack methods—adversarial examples, model poisoning, and data exfiltration—and outlines your legal obligations for breach response.

Understanding the AI Attack Surface

Why Deepfakes and AI-Generated Media Are a Business Issue?

Deepfakes—the use of advanced artificial intelligence to create realistic but fake videos, images, or audio—are no longer just an internet curiosity. In 2024 and 2025, corporate security teams, compliance officers, and general counsel have seen a surge in fraud attempts and reputational crises driven by AI-generated content. From executives’ voices cloned to authorize fraudulent wire transfers, to fake customer reviews undermining brand trust, synthetic media is now a mainstream threat. Businesses that fail to anticipate this risk face financial losses, regulatory exposure, and reputational damage.

Understanding Deepfakes, Synthetic Media, and Fraud Risks

What Is Shadow AI?

Artificial Intelligence (AI) has become a powerful tool for businesses of every size. From chatbots that streamline customer service to data-driven algorithms that optimize supply chains, AI adoption is skyrocketing. However, alongside official AI deployments another trend called “Shadow AI” is growing.

Shadow AI refers to the use of artificial intelligence tools and systems inside an organization without official approval, oversight, or governance. Much like “shadow IT” in past decades, where employees adopted unauthorized apps or devices, shadow AI creates hidden cybersecurity, privacy, and compliance risks for businesses. With the rise of easily accessible generative AI platforms like ChatGPT, Bard, Claude, and open-source models, employees are bringing these tools into daily workflows — often without realizing the potential consequences.

Artificial Intelligence (AI) is no longer just a tech buzzword since it’s embedded in business operations, government processes, healthcare, finance, and even our daily communications. However, as AI adoption accelerates, so do the legal, regulatory, and compliance challenges for companies, developers, and professionals. AI laws are evolving faster than ever this year. Governments around the world are introducing new rules to address transparency, bias, privacy, and accountability in AI systems. For business owners, executives, and legal teams, staying ahead of these changes is no longer optional — it’s essential. This article outlines the most important AI legal trends for 2025, why they matter, and how your organization can prepare.

The EU AI Act Begins to Take Effect

The EU AI Act, approved in 2024, is the world’s first comprehensive AI regulation. It classifies AI systems into risk categories — minimal, limited, high, and unacceptable — with different compliance obligations for each.

As artificial intelligence (AI) rapidly transforms industries, from healthcare and finance to law enforcement and education, questions of risk, responsibility, and trust loom large. To address these concerns, the National Institute of Standards and Technology (NIST) released the AI Risk Management Framework (AI RMF) in January 2023 — a voluntary but powerful tool designed to help organizations develop and deploy trustworthy AI systems. While the AI RMF is not a regulatory mandate, its adoption signals a growing consensus around best practices in AI governance. It provides a flexible and principle-based structure that can be used by companies, government agencies, and developers to identify and mitigate the unique risks associated with AI technologies.

What Is the NIST AI RMF?

The AI RMF is a risk-based, socio-technical framework that guides organizations in managing the many facets of AI risk — not just technical errors or security issues, but also fairness, transparency, privacy, and societal impact. It is a voluntary guidance framework developed by the NIST to help organizations identify, assess, manage, and minimize risks associated with artificial intelligence (AI) systems. The AI RMF helps organizations (1) understand and manage AI-related risks across the lifecycle; (2) build transparency, accountability, fairness, and security into AI systems; and (3) align with global AI governance trends (e.g., EU AI Act, OECD AI Principles). It is sector-agnostic and technology-neutral, meaning it can be applied to any organization building or using AI, whether in healthcare, finance, education, defense, or consumer technologies.

Artificial Intelligence (AI) has rapidly transformed from a niche area of computer science into a foundational technology influencing nearly every sector of society. From predictive algorithms in healthcare and finance to autonomous vehicles and generative AI tools like ChatGPT, AI systems are reshaping how we live, work, and interact with technology. Yet with this explosive growth comes a critical challenge: how do we govern AI technologies in a way that fosters innovation while protecting human rights, privacy, and safety?

This question has sparked global efforts to create legal frameworks for AI. However, the pace of AI development often outstrips the speed of regulation, leaving governments scrambling to catch up. As AI systems become more powerful and pervasive, robust and thoughtful legal frameworks are essential to ensure that these technologies serve the public interest.

Understanding AI Technologies

Introduction

In the digital age, the way we perceive, transfer, and assign value to assets is undergoing a dramatic transformation. One of the most significant innovations driving this shift is the Non-Fungible Token (NFT) — a type of cryptographic asset that represents ownership of a unique item or piece of content on a blockchain. Unlike cryptocurrencies such as Bitcoin or Ethereum, which are fungible (interchangeable and uniform in value), NFTs are non-fungible, meaning each token is unique and cannot be exchanged on a one-to-one basis with another NFT. While NFTs initially gained attention for digital art and collectibles, their potential are more expansive. This article explores the underlying technology behind NFTs and how they can enhance various types of transactions.

What is an NFT?

Business Email Compromise (BEC) is a sophisticated cybercrime that targets businesses and individuals performing legitimate transfer-of-funds requests. Attackers employ tactics such as email spoofing, phishing, and social engineering to impersonate trusted entities—like executives, vendors, or legal representatives—to deceive victims into transferring money or sensitive information.

Common BEC Techniques

• Email Spoofing: Crafting emails that appear to originate from trusted sources

Hackers use a variety of methods to compromise computers, email accounts, and bank accounts, typically exploiting vulnerabilities in systems, weak security practices, or human error. Below are some of the most common techniques hackers use to gain unauthorized access:

1. Phishing

– Method: Hackers send fraudulent emails, text messages, or websites that appear to be from legitimate sources (such as banks, email providers, or well-known companies). These messages trick users into providing sensitive information, such as usernames, passwords, or credit card details.