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Showing posts with label Legal risks. Show all posts

The Price of Cyberattacks: Why the Real Damage Goes Beyond the Ransom

 




When news breaks about a cyberattack, the ransom demand often steals the spotlight. It’s the most visible figure, millions demanded, negotiations unfolding, and sometimes, payment made. But in truth, that amount only scratches the surface. The real costs of a cyber incident often emerge long after the headlines fade, in the form of business disruptions, shaken trust, legal pressures, and a long, difficult road to recovery.

One of the most common problems organizations face after a breach is the communication gap between technical experts and senior leadership. While the cybersecurity team focuses on containing the attack, tracing its source, and preserving evidence, the executives are under pressure to reassure clients, restore operations, and navigate complex reporting requirements.

Each group works with valid priorities, but without coordination, efforts can collide. A system that’s isolated for forensic investigation may also be the one that the operations team needs to serve customers. This misalignment is avoidable if organizations plan beyond technology by assigning clear responsibilities across departments and conducting regular crisis simulations to ensure a unified response when an attack hits.

When systems go offline, the impact ripples across every department. A single infected server can halt manufacturing lines, delay financial transactions, or force hospitals to revert to manual record-keeping. Even after the breach is contained, lost time translates into lost revenue and strained customer relationships.

Many companies underestimate downtime in their recovery strategies. Backup plans often focus on restoring data, but not on sustaining operations during outages. Every organization should ask: Can employees access essential tools if systems are locked? Can management make decisions without their usual dashboards? If those answers are uncertain, then the recovery plan is incomplete.

Beyond financial loss, cyber incidents leave a lasting mark on reputation. Customers and partners may begin to question whether their information is safe. Rebuilding that trust requires transparent, timely, and fact-based communication. Sharing too much before confirming the facts can create confusion; saying too little can appear evasive.

Recovery also depends on how well a company understands its data environment. If logs are incomplete or investigations are slow, regaining credibility becomes even harder. The most effective organizations balance honesty with precision, updating stakeholders as verified information becomes available.

The legal consequences of a cyber incident often extend further than companies expect. Even if a business does not directly store consumer data, it may still have obligations under privacy laws, vendor contracts, or insurance terms. State and international regulations increasingly require timely disclosure of breaches, and failing to comply can result in penalties.

Engaging legal and compliance teams before a crisis ensures that everyone understands the organization’s obligations and can act quickly under pressure.

Cybersecurity is no longer just an IT issue; it’s a core business concern. Effective protection depends on organization-wide preparedness. That means bridging gaps between departments, creating holistic response plans that include legal and communication teams, and regularly testing how those plans perform under real-world pressure.

Businesses that focus on resilience, not just recovery, are better positioned to minimize disruption, maintain trust, and recover faster if a cyber incident occurs.



What are the Legal Implications and Risks of Generative AI?


In the ever-evolving AI landscape, dealing with the changing regulations and securing data privacy has become a new challenge. With more efficient human capabilities, AI must not replace humans, especially in a world where its standards are still developing globally. 

There are certain risks that the unchecked generative AI possesses with the overabundant information it may hold. Companies run the risk of disclosing their valuable assets when they feed private, sensitive data into open AI models. Some businesses choose to localize AI models on their systems and train them using their confidential data in order to reduce this danger. However, for best outcomes, such a strategy necessitates a well-organized data architecture.

Risks of Unchecked Generative AI

The appealing elements of generative AI and Large Language Models (LLMs) are their capabilities to compile information to produce fresh ideas, but these skills also carry inherent risks. If not carefully handled, gen AI can unintentionally result in issues like: 

Personal Data Security 

AI systems must handle personal data with the utmost care, especially sensitive or special category personal data. Concerns about unintentional data leaks that could lead to data privacy violations are raised by the growing integration of marketing and consumer data into LLMs.

Contractual Violations 

It is occasionally illegal to use consumer data in AI systems, which has negative legal repercussions. As companies adopt AI, they must carefully negotiate this treacherous terrain to ensure they uphold contractual commitments.

Customer Transparency and Disclosure 

The goals of current and potential future AI regulations focus on a transparent and lucid disclosure of AI technology. For instance, the business must disclose whether a person or an AI is handling a customer's engagement with a chatbot on a support website. Maintaining trust and upholding ethical standards depend on adherence to such restrictions.

Legal Challenges and Risks for Businesses 

Recent legal actions against eminent AI companies highlight the significance of handling data responsibly. The importance of strict data governance and transparency is highlighted by these lawsuits, which include class action cases involving copyright infringement, consumer protection, and data protection issues. They also suggest possible conditions for exposing the origins of AI training data.

Since their use of copyrighted data to build and train their models, AI giants have been the main targets of various lawsuits. Allegations of copyright infringement, consumer protection violations, and data protection legislation violations are made in recent class action lawsuits filed in the Northern District of California, including one filed on behalf of authors and another on behalf of victim users. These submissions emphasize the value of treating data responsibly and could indicate that in the future it will be necessary to identify the sources of training data.

Moreover, businesses possess serious risks when they significantly rely on AI models, not just AI developers like OpenAI. The case of how many of the apps implement improper AI model training may taint entire products. The parent business Everalbum was forced to destroy improperly gathered data and AI models after the Federal Trade Commission (FTC) accused Everalbum of misleading consumers about the use of face recognition technology and data retention. This forced Everalbum to cease in 2020.

How to Mitigate AI Risks? 

Despite the legal challenges, CEOs are under pressure to adopt generative AI if they wish to increase their business’ productivity. Businesses can create best practices and get ready for new requirements by using the frameworks and legislation currently in place. AI systems are covered by provisions in existing data protection regulations, such as those requiring transparency, notice, and the protection of individual privacy rights. Some of these best practices involve:

  • Transparency and Documentation: Businesses are recommended to clearly mention the AI usage, and document AI logic, applications and potential impacts on the data subjects. Also, businesses must keep a record of data transactions and detailed logs of confidential information in order to maintain proper governance and data security.
  • Localizing AI Models: By ensuring that models are trained on pertinent, organization-specific information, internal localization and training with private data can lower data security risks and boost efficiency.
  • Discovering and Connecting: Companies must utilize generative AI to unveil new perspectives and create unexpected connections across different departments and information silos.
  • Preserving Human Element: Gen AI should improve human performance rather than completely replace it. To reduce model biases and data inaccuracies, human monitoring, critical decision review, and content verification of AI-created information are essential.