Sep 09 2025

Exploring AI security, privacy, and the pressing regulatory gaps—especially relevant to today’s fast-paced AI landscape

Category: AI,AI Governance,Information Security — disc7 @ 12:44 pm

Featured Read: “Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity“

  • Overview: This academic paper examines the growing ethical and regulatory challenges brought on by AI’s integration with cybersecurity. It traces the evolution of AI regulation, highlights pressing concerns—like bias, transparency, accountability, and data privacy—and emphasizes the tension between innovation and risk mitigation.
  • Key Insights:
    • AI systems raise unique privacy/security issues due to their opacity and lack of human oversight.
    • Current regulations are fragmented—varying by sector—with no unified global approach.
    • Bridging the regulatory gap requires improved AI literacy, public engagement, and cooperative policymaking to shape responsible frameworks.
  • Source: Authored by Vikram Kulothungan, published in January 2025, this paper cogently calls for a globally harmonized regulatory strategy and multi-stakeholder collaboration to ensure AI’s secure deployment.

Why This Post Stands Out

  • Comprehensive: Tackles both cybersecurity and privacy within the AI context—not just one or the other.
  • Forward-Looking: Addresses systemic concerns, laying the groundwork for future regulation rather than retrofitting rules around current technology.
  • Action-Oriented: Frames AI regulation as a collaborative challenge involving policymakers, technologists, and civil society.

Additional Noteworthy Commentary on AI Regulation

1. Anthropic CEO’s NYT Op-ed: A Call for Sensible Transparency

Anthropic CEO Dario Amodei criticized a proposed 10-year ban on state-level AI regulation as “too blunt.” He advocates a federal transparency standard requiring AI developers to disclose testing methods, risk mitigation, and pre-deployment safety measures.

2. California’s AI Policy Report: Guarding Against Irreversible Harms

A report commissioned by Governor Newsom warns of AI’s potential to facilitate biological and nuclear threats. It advocates “trust but verify” frameworks, increased transparency, whistleblower protections, and independent safety validation.

3. Mutually Assured Deregulation: The Risks of a Race Without Guardrails

Gilad Abiri argues that dismantling AI safety oversight in the name of competition is dangerous. Deregulation doesn’t give lasting advantages—it undermines long-term security, enabling proliferation of harmful AI capabilities like bioweapon creation or unstable AGI.

Broader Context & Insights

  • Fragmented Landscape: U.S. lacks unified privacy or AI laws; even executive orders remain limited in scope.
  • Data Risk: Many organizations suffer from unintended AI data exposure and poor governance despite having some policies in place.
  • Regulatory Innovation: Texas passed a law focusing only on government AI use, signaling a partial step toward regulation—but private sector oversight remains limited.
  • International Efforts: The Council of Europe’s AI Convention (2024) is a rare international treaty aligning AI development with human rights and democratic values.
  • Research Proposals: Techniques like blockchain-enabled AI governance are being explored as transparency-heavy, cross-border compliance tools.

Opinion

AI’s pace of innovation is extraordinary—and so are its risks. We’re at a crossroads where lack of regulation isn’t a neutral stance—it accelerates inequity, privacy violations, and even public safety threats.

What’s needed:

  1. Layered Regulation: From sector-specific rules to overarching international frameworks; we need both precision and stability.
  2. Transparency Mandates: Companies must be held to explicit standards—model testing practices, bias mitigation, data usage, and safety protocols.
  3. Public Engagement & Literacy: AI literacy shouldn’t be limited to technologists. Citizens, policymakers, and enforcement institutions must be equipped to participate meaningfully.
  4. Safety as Innovation Avenue: Strong regulation doesn’t kill innovation—it guides it. Clear rules create reliable markets, investor confidence, and socially acceptable products.

The paper “Securing the AI Frontier” sets the right tone—urging collaboration, ethics, and systemic governance. Pair that with state-level transparency measures (like Newsom’s report) and critiques of over-deregulation (like Abiri’s essay), and we get a multi-faceted strategy toward responsible AI.

Anthropic CEO says proposed 10-year ban on state AI regulation ‘too blunt’ in NYT op-ed

California AI Policy Report Warns of ‘Irreversible Harms’ 

Responsible AI in the Age of Generative Models: Governance, Ethics and Risk Management 

AI Governance: Applying AI Policy and Ethics through Principles and Assessments

AIMS and Data Governance â€“ Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative. 

DISC InfoSec previous posts on AI category

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Tags: AI privacy, AI Regulations, AI security, AI standards

Jul 02 2025

Emerging AI Security and Privacy Challenges and Risks

Category: AI,data security,Information Privacy,Information Security — disc7 @ 6:59 am

Several posts published recently discuss AI security and privacy, highlighting different perspectives and concerns. Here’s a summary of the most prominent themes and posts:

Emerging Concerns and Risks:

  • Growing Anxiety around AI Data Privacy: A recent survey found that a significant majority of Americans (91%) are concerned about social media platforms using their data to train AI models, with 69% aware of this practice.
  • AI-Powered Cyber Threats on the Rise: AI is increasingly being used to generate sophisticated phishing attacks and malware, making it harder to distinguish between legitimate and malicious content.
  • Gap between AI Adoption and Security Measures: Many organizations are quickly adopting AI but lag in implementing necessary security controls, creating a major vulnerability for data leaks and compliance issues.
  • Deepfakes and Impersonation Scams: The use of AI in creating realistic deepfakes is fueling a surge in impersonation scams, increasing privacy risks.
  • Opaque AI Models and Bias: The “black box” nature of some AI models makes it difficult to understand how they make decisions, raising concerns about potential bias and discrimination. 

Regulatory Developments:

  • Increasing Regulatory Scrutiny: Governments worldwide are focusing on regulating AI, with the EU AI Act setting a risk-based framework and China implementing comprehensive regulations for generative AI.
  • Focus on Data Privacy and User Consent: New regulations emphasize data minimization, purpose limitation, explicit user consent for data collection and processing, and requirements for data deletion upon request. 

Best Practices and Mitigation Strategies:

  • Robust Data Governance: Organizations must establish clear data governance frameworks, including data inventories, provenance tracking, and access controls.
  • Privacy by Design: Integrating privacy considerations from the initial stages of AI system development is crucial.
  • Utilizing Privacy-Preserving Techniques: Employing techniques like differential privacy, federated learning, and synthetic data generation can enhance data protection.
  • Continuous Monitoring and Threat Detection: Implementing tools for continuous monitoring, anomaly detection, and security audits helps identify and address potential threats.
  • Employee Training: Educating employees about AI-specific privacy risks and best practices is essential for building a security-conscious culture. 

Specific Mentions:

  • NSA’s CSI Guidance: The National Security Agency (NSA) released joint guidance on AI data security, outlining best practices for organizations.
  • Stanford’s 2025 AI Index Report: This report highlighted a significant increase in AI-related privacy and security incidents, emphasizing the need for stronger governance frameworks.
  • DeepSeek AI App Risks: Experts raised concerns about the DeepSeek AI app, citing potential security and privacy vulnerabilities. 

Based on current trends and recent articles, it’s evident that AI security and privacy are top-of-mind concerns for individuals, organizations, and governments alike. The focus is on implementing strong data governance, adopting privacy-preserving techniques, and adapting to evolving regulatory landscapes. 

The rapid rise of AI has introduced new cyber threats, as bad actors increasingly exploit AI tools to enhance phishing, social engineering, and malware attacks. Generative AI makes it easier to craft convincing deepfakes, automate hacking tasks, and create realistic fake identities at scale. At the same time, the use of AI in security tools also raises concerns about overreliance and potential vulnerabilities in AI models themselves. As AI capabilities grow, so does the urgency for organizations to strengthen AI governance, improve employee awareness, and adapt cybersecurity strategies to meet these evolving risks.

There is a lack of comprehensive federal security and privacy regulations in the U.S., but violations of international standards often lead to substantial penalties abroad for U.S. organizations. Penalties imposed abroad effectively become a cost of doing business for U.S. organizations.

Meta has faced dozens of fines and settlements across multiple jurisdictions, with at least a dozen significant penalties totaling tens of billions of dollars/euros cumulatively.

Artificial intelligence (AI) and large language models (LLMs) emerging as the top concern for security leaders. For the first time, AI, including tools such as LLMs, has overtaken ransomware as the most pressing issue.

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Tags: AI privacy, AI Security Essentials, AI Security Risks, AI-Driven Security

Apr 01 2025

Things You may not want to Tell ChatGPT

Category: AI,Information Privacy — disc7 @ 8:37 am

​Engaging with AI chatbots like ChatGPT offers numerous benefits, but it’s crucial to be mindful of the information you share to safeguard your privacy. Sharing sensitive data can lead to security risks, including data breaches or unauthorized access. To protect yourself, avoid disclosing personal identity details, medical information, financial account data, proprietary corporate information, and login credentials during your interactions with ChatGPT. ​

Chat histories with AI tools may be stored and could potentially be accessed by unauthorized parties, especially if the AI company faces legal actions or security breaches. To mitigate these risks, it’s advisable to regularly delete your conversation history and utilize features like temporary chat modes that prevent the saving of your interactions. ​

Implementing strong security measures can further enhance your privacy. Use robust passwords and enable multifactor authentication for your accounts associated with AI services. These steps add layers of security, making unauthorized access more difficult. ​

Some AI companies, including OpenAI, provide options to manage how your data is used. For instance, you can disable model training, which prevents your conversations from being utilized to improve the AI model. Additionally, opting for temporary chats ensures that your interactions aren’t stored or used for training purposes. ​

For tasks involving sensitive or confidential information, consider using enterprise versions of AI tools designed with enhanced security features suitable for professional environments. These versions often come with stricter data handling policies and provide better protection for your information.

By being cautious about the information you share and utilizing available privacy features, you can enjoy the benefits of AI chatbots like ChatGPT while minimizing potential privacy risks. Staying informed about the data policies of the AI services you use and proactively managing your data sharing practices are key steps in protecting your personal and sensitive information.

For further details, access the article here

DISC InfoSec’s earlier post on the AI topic

What You Are Not Told About ChatGPT: Key Insights into the Inner Workings of ChatGPT & How to Get the Most Out of It

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Tags: AI Ethics, AI privacy, ChatGPT, Digital Ethics, privacy

Jan 29 2025

Basic Principle to Enterprise AI Security

Category: AI — disc7 @ 12:24 pm

Securing AI in the Enterprise: A Step-by-Step Guide

  1. Establish AI Security Ownership
    Organizations must define clear ownership and accountability for AI security. Leadership should decide whether AI governance falls under a cross-functional committee, IT/security teams, or individual business units. Establishing policies, defining decision-making authority, and ensuring alignment across departments are key steps in successfully managing AI security from the start.
  2. Identify and Mitigate AI Risks
    AI introduces unique risks, including regulatory compliance challenges, data privacy vulnerabilities, and algorithmic biases. Organizations must evaluate legal obligations (such as GDPR, HIPAA, and the EU AI Act), implement strong data protection measures, and address AI transparency concerns. Risk mitigation strategies should include continuous monitoring, security testing, clear governance policies, and incident response plans.
  3. Adopt AI Security Best Practices
    Businesses should follow security best practices, such as starting with small AI implementations, maintaining human oversight, establishing technical guardrails, and deploying continuous monitoring. Strong cybersecurity measures—such as encryption, access controls, and regular security audits—are essential. Additionally, comprehensive employee training programs help ensure responsible AI usage.
  4. Assess AI Needs and Set Measurable Goals
    AI implementation should align with business objectives, with clear milestones set for six months, one year, and beyond. Organizations should define success using key performance indicators (KPIs) such as revenue impact, efficiency improvements, and compliance adherence. Both quantitative and qualitative metrics should guide AI investments and decision-making.
  5. Evaluate AI Tools and Security Measures
    When selecting AI tools, organizations must assess security, accuracy, scalability, usability, and compliance. AI solutions should have strong data protection mechanisms, clear ROI, and effective customization options. Evaluating AI tools using a structured approach ensures they meet security and business requirements.
  6. Purchase and Implement AI Securely
    Before deploying AI solutions, businesses must ask key questions about effectiveness, performance, security, scalability, and compliance. Reviewing trial options, pricing models, and regulatory alignment (such as GDPR or CCPA compliance) is critical to selecting the right AI tool. AI security policies should be integrated into the organization’s broader cybersecurity framework.
  7. Launch an AI Pilot Program with Security in Mind
    Organizations should begin with a controlled AI pilot to assess risks, validate performance, and ensure compliance before full deployment. This includes securing high-quality training data, implementing robust authentication controls, continuously monitoring performance, and gathering user feedback. Clear documentation and risk management strategies will help refine AI adoption in a secure and scalable manner.

By following these steps, enterprises can securely integrate AI, protect sensitive data, and ensure regulatory compliance while maximizing AI’s potential.

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Tags: AI Governance, AI privacy, AI Risk Management, AI security

Nov 13 2024

How CISOs Can Drive the Adoption of Responsible AI Practices

Category: AI,Information Security — disc7 @ 11:47 am

Amid the rush to adopt AI, leaders face significant risks if they lack an understanding of the technology’s potential cyber threats. A PwC survey revealed that 40% of global leaders are unaware of generative AI’s risks, posing potential vulnerabilities. CISOs should take a leading role in assessing, implementing, and overseeing AI, as their expertise in risk management can ensure safer integration and focus on AI’s benefits. While some advocate for a chief AI officer, security remains integral, emphasizing the CISO’s/ vCISO’S strategic role in guiding responsible AI adoption.

CISOs are crucial in managing the security and compliance of AI adoption within organizations, especially with evolving regulations. Their role involves implementing a security-first approach and risk management strategies, which includes aligning AI goals through an AI consortium, collaborating with cybersecurity teams, and creating protective guardrails.

They guide acceptable risk tolerance, manage governance, and set controls for AI use. Whether securing AI consumption or developing solutions, CISOs must stay updated on AI risks and deploy relevant resources.

A strong security foundation is essential, involving comprehensive encryption, data protection, and adherence to regulations like the EU AI Act. CISOs enable informed cross-functional collaboration, ensuring robust monitoring and swift responses to potential threats.

As AI becomes mainstream, organizations must integrate security throughout the AI lifecycle to guard against GenAI-driven cyber threats, such as social engineering and exploitation of vulnerabilities. This requires proactive measures and ongoing workforce awareness to counter these challenges effectively.

“AI will touch every business function, even in ways that have yet to be predicted. As the bridge between security efforts and business goals, CISOs serve as gatekeepers for quality control and responsible AI use across the business. They can articulate the necessary ground for security integrations that avoid missteps in AI adoption and enable businesses to unlock AI’s full potential to drive better, more informed business outcomes. “

You can read the full article here

CISOs play a pivotal role in guiding responsible AI adoption to balance innovation with security and compliance. They need to implement security-first strategies and align AI goals with organizational risk tolerance through stakeholder collaboration and robust risk management frameworks. By integrating security throughout the AI lifecycle, CISOs/vCISOs help protect critical assets, adhere to regulations, and mitigate threats posed by GenAI. Vigilance against AI-driven attacks and fostering cross-functional cooperation ensures that organizations are prepared to address emerging risks and foster safe, strategic AI use.

Need expert guidance? Book a free 30-minute consultation with a vCISO.

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Tags: AI privacy, AI security impact, AI threats, CISO, vCISO

Jul 20 2023

How do you solve privacy issues with AI? It’s all about the blockchain

Category: AI,Blockchain,Information Privacy — disc7 @ 9:18 am
How do you solve privacy issues with AI? It’s all about the blockchain

How do you solve privacy issues with AI? It’s all about the blockchain

Data is the lifeblood of artificial intelligence (AI), and the power that AI brings to the business world — to unearth fresh insights, increase speed and efficiency, and multiply effectiveness — flows from its ability to analyze and learn from data. The more data AI has to work with, the more reliable its results will be.

Feeding AI’s need for data means collecting it from a wide variety of sources, which has raised concerns about AI gathering, processing, and storing personal data. The fear is that the ocean of data flowing into AI engines is not properly safeguarded.

Are you donating your personal data to generative AI platforms?

While protecting the data that AI tools like ChatGPT is collecting against breaches is a valid concern, it is actually only the tip of the iceberg when it comes to AI-related privacy issues. A more poignant issue is data ownership. Once you share information with a generative AI tool like Bard, who owns it?

Those who are simply using generative AI platforms to help craft better social posts may not understand the connection between the services they offer and personal data security. But consider the person who is using an AI-driven chatbot to explore treatment for a medical condition, learn about remedies for a financial crisis, or find a lawyer. In the course of the exchange, those users will most likely share some personal and sensitive information.

Every query posed to an AI platform becomes part of that platform’s data set without regard to whether or not it is personal or sensitive. ChatGPT’s privacy policy makes it clear: “When you use our Services, we collect Personal Information that is included in the input, file uploads, or feedback that you provide to our Services.” It also says: “In certain circumstances we may provide your Personal Information to third parties without further notice to you, unless required by the law
”

Looking to blockchain for data privacy solutions

While the US government has called for an “AI Bill of Rights” designed to protect sensitive data, it has yet to provide the type of regulations that protect its ownership. Consequently, Google and Microsoft have full ownership over the data that their users provide as they comb the web with generative AI platforms. That data empowers them to train their AI models, but also to get to understand you better.

Those looking for a way to gain control of their data in the age of AI can find a solution in blockchain technology. Commonly known as the foundation of cryptocurrency, blockchain can also be used to allow users to keep their personal data safe. By empowering a new type of digital identity management — known as a universal identity layer — blockchain allows you to decide how and when your personal data is shared.

Blockchain technology brings a number of factors into play that boost the security of personal data. First, it is decentralized, meaning that data is not stored in a centralized database and is not subject to its vulnerabilities with blockchain.

Blockchain also supports smart contracts, which are self-executing contracts that have the terms of an agreement written into their code. If the terms aren’t met, the contract does not execute, allowing for data stored on the blockchain to be utilized only in the way in which the owner stipulates.

Enhanced security is another factor that blockchain brings to data security efforts. The cryptographic techniques it utilizes allow users to authenticate their identity without revealing sensitive data.

Leveraging these factors to create a new type of identification framework gives users full control of who can use and view their information, for what purposes, and for how long. Once in place, this type of identity system could even be used to allow users to monetize their data, charging large language models (LLMs) like OpenAI and Google Bard to benefit from the use of personal data.

Ultimately, AI’s ongoing needs may lead to the creation of platforms where users offer their data to LLMs for a fee. A blockchain-based universal identity layer would allow the user to choose who gets to use it, toggling access on and off at will. If you decide you don’t like the business practices Google has been employing over the past two months, you can cut them off at the source.

That type of AI model illustrates the power that comes from securing data on a decentralized network. It also reveals the killer use case of blockchain that is on the horizon.

Image credit: tampatra@hotmail.com/depositphotos.com

Aaron Rafferty is the CEO of Standard DAO and Co-Founder of BattlePACs, a subsidiary of Standard DAO. BattlePACs is a technology platform that transforms how citizens engage in politics and civil discourse. BattlePACs believes participation and conversations are critical to moving America toward a future that works for everyone.

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