Usage and Protection of Artificial Intelligence(AI)

Wednesday, June 11, 2025

Defending AI Systems Against Attacks

Let us try to imagine a magnificent enchanted forest where is planted an ancient monumental tree1, with its roots deep and vast, holding the immemorial wisdom accumulated throughout the ages of humankind’s history. This primeval massive tree is an artificial intelligence (AI) – intricately multiplex, powerful, ingenuous and in full growth, with its roots capable of penetrating and enriching the multiple layers of terra firma around it. Its branches stretch outward and upward, offering shade and fruits to those who are seeking knowledge, experimentation, creativity and innovation. Nonetheless, like any mighty force existing across Mother Nature, the tree of artificial intelligence needs careful guardianship. If left unchecked, its roots could crack foundations, its shade could grow too dense, and its unrestrained growth might overshadow the delicate ecosystem that relies on the principle of balance governing Mother Nature. Sadly, crawling poisonous substances – symbolic of corrupted information or malevolent manipulations – could infect its roots, distorting the vibrant leaves and causing the branches to twist unnaturally. The fruits of the tree of artificial intelligence – which once represented creativity, innovation and progress – could become toxic.

Fortunately, inside this enchanted and magnificent forest, the watchful guardians acting as protectors – people of all backgrounds, knowledge and wisdom seekers, scientists in general, computer scientists in particular, ethicists, policymakers, legislators, organizations implementing standards, professional certification bodies, cybersecurity risk management experts, and everyday users of information technology – are the caretakers of that ancient monumental tree called AI. In their role as protectors, those conscientious guardians nurture the development, evolution and training of that massive primeval tree, ensuring it flourishes without harming the harmony and natural balance of the mesmerizing forest. Some of those caretakers, captivated by the outstanding stature of that innovative tree, approach the perennial plant with amazement, seeing its potential to illuminate and uplift the human species. Some of those guardians, circumspect and cautious of the unconstrained power of that majestic tree, commit themselves to carefully write protective documents – principles, rules and regulations, ethical frameworks, operational policies, defensive measures, cybersecurity risk management guidelines, development and deployment strategies, and action plan safeguards – to ensure that AI remains a force for human welfare and not a power to decimate life on our planet Earth.

Will those caretakers, acting as protectors, guide and defend this tree with wisdom by fostering a future where its shade brings comfort but never darkness? Or will negligence, greed, malevolence and mistreatment lead to a wild, undomesticated force, reshaping the terra firma of our blue planet in unforeseen ways?2In our nowadays planet Earth, where millions and millions of humans are using on a daily basis all types of devices and apps, how could hardworking Canadian entrepreneurs wisely utilize, benefit from and protect AI in their daily business operations? What are some of the many cybersecurity threats to AI systems, and how could our SMEs and other organizations throughout Canada prevent them from happening? Our May 2025 Newsletter has been punctiliously written to provide relevant answers to those consequential questions.

1 – This emblematic characterization of artificial intelligence (AI) as an ancient monumental tree has been inspired by the narrative of the Tree of Knowledge in the Garden of Eden, chronicled thousands of years ago in the Bible – Gen 2:8-25. Monograph reference:United Bible Societies (UBS) – The Holy Bible: New King James Version (NKJV), Thomas Nelson Publishers Inc., USA, 2016, 1578 pages.

2 – Nils J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Paperback illustrated Edition published on the 30th of October 2009 by Cambridge University Press, United Kingdom, 580 pages. Quest artificial intelligence | Artificial intelligence and natural language processing | Cambridge University Press

Some Best Practices When Using Artificial Intelligence

Artificial intelligence systems are readily accessible online. For SMEs, adopting strong data protection practices when using AI is essential to safeguard privacy and ensure security. Below is a summary of key best practices1,2,3  to follow:

  1. Establish an AI Usage Policy: Develop a clear internal policy that outlines acceptable AI use, data handling and storage procedures, and review protocols. A well-defined policy ensures consistency, reduces risk, and provides a framework for responsible AI adoption and deployment.
  2. Choose Reputable AI Providers: Select AI vendors that clearly communicate their data handling and storage practices, and demonstrate compliance with recognized security standards. Reputable providers typically implement robust safeguards such as data encryption and maintain explicit policies regarding the use of client information. To further enhance privacy protections, some vendors offer commercial licensing options. These paid services often include stricter privacy terms, specialized support, and well-defined contractual obligations. By choosing a commercial license, organizations gain greater control over how their data is managed and stored, significantly reducing the risk of unauthorized access or misuse. Regularly auditing vendor performance is also essential to ensure ongoing compliance and accountability.
  3. Prioritize Data Privacy and Security: When using online AI tools, protecting sensitive information must be a top priority. SMEs should avoid sharing personal or confidential data and, where possible, anonymize inputs before submitting them to AI systems. Compliance with data protection regulations—such as Canada’s PIPEDA (Personal Information Protection and Electronic Documents Act) and Québec Law 25, which strengthens individual privacy rights—is essential for maintaining legal integrity and customer trust.

To minimize risk, never share the following types of information with public or third-party AI tools:

  • Personally Identifiable Information (PII): Names, email addresses, phone numbers, social insurance numbers (SINs), etc.
  • Logging Credentials: Passwords, Application Programming Interface (API) keys, authentication tokens.
  • Internal Assets: Source code, system configurations, or internal documentation.
  • Proprietary Business Information: Trade secrets, patents, or strategic plans.

Always treat anything entered into an AI tool as potentially public by default. A notable example of the consequences of poor data handling occurred when Samsung employees unintentionally leaked confidential source code and internal documents by copying and pasting them into ChatGPT for review4. This incident led Samsung to prohibit the internal use of generative AI tools entirely, highlighting the real-world risks of data exposure through AI platforms.

  1. Use Right of Entry Controls and Logging: Restrict access to AI tools to only those who need it and implement logging to track usage. This helps maintain accountability and allows for quick responses if any misuse or data breach occurs.
  2. Train Staff on Responsible AI Use: Educating employees about the ethical and practical aspects of AI use is vital. Staff should understand what data is appropriate to share and how to use AI tools responsibly to avoid unintended consequences.
  3. Understand the AI Tool’s Capabilities and Limitations: Before integrating an AI tool into your workflow, take time to understand what it can and cannot do. Misusing AI beyond its intended purpose can lead to errors, inefficiencies, or even reputational damage. An example of this: the refund given by Air Canada to one of its customers (see on next page the article written by Jason Proctor, CBC News Reporter).
  4. Review and Monitor AI Outputs: AI-generated content should never be accepted at face value. It is essential to carefully review outputs for accuracy, relevance, and potential bias. Human oversight plays a critical role in validating AI responses and ensuring that decisions informed by AI are sound, ethical, and aligned with organizational goals. Regular monitoring helps maintain quality and accountability in AI-assisted processes.

1 – Brian Spisak, Louis B. Rosenberg, and Max Bielby. Harvard Business Review Technology and Analytics Awareness Series Scientific Paper: 13 Principles for Using AI Responsibly. Awareness Series Scientific Paper published by Harvard Business Review (HBR) on the 30th of June 2023. 13 Principles for Using AI Responsibly

2 – Colette Stallbaumer. Microsoft Corporation. Empowering Responsible AI Practices within Microsoft AI for Microsoft 365 WorkLab. User manual guidelines published on the 23rd of April 2025. Empowering responsible AI practices | Microsoft AI

3 – Abraham Gaur. Best Practices for Leveraging Artificial Intelligence and Machine Learning in 2023. Awareness Article for Start Ups published via TechCrunch on the 17th of March 2023. Best practices for leveraging artificial intelligence and machine learning in 2023 | TechCrunch

4 – Itamar Golan. Prompt Security Platform for GenAI Security. 8 Real World Incidents Related to AI. GenAI Security Paper published on the 31st of August 2024. 8 Real World Incidents Related to AI

Developing AI with Security in Mind: A General Guide for SMEs

This section is specifically designed for small and medium-sized enterprises (SMEs) that are developing AI-based products or training their own AI models, with a focus on defending these systems against cyber threats. Unlike general users of AI tools, SMEs involved in AI development face heightened risks related to data breaches, AI model manipulation, and adversarial attacks. These organizations must go beyond ethical and regulatory compliance by embedding cybersecurity into every stage of the AI lifecycle. This includes securing high-quality, unbiased training data, implementing robust access controls, documenting AI model behavior, and deploying techniques to detect and mitigate adversarial inputs. By proactively addressing these vulnerabilities, SMEs can build resilient AI systems that not only perform reliably but also withstand evolving cyber threats—ensuring both business continuity and user trust.

Real-World Examples of Artificial Intelligence Abuses

Chevrolet Dealership

A Chevrolet AI chatbot pricing error occurred when a dealership in Watsonville, California (USA) deployed a customer service chatbot powered by ChatGPT on its website. The chatbot was intended to assist with basic inquiries like scheduling service or helping customers explore vehicle options. Nevertheless, it lacked proper safeguards and prompt restrictions, making it vulnerable to manipulation1  A user named Chris Bakke, a self-described “senior prompt engineer,” exploited this by instructing the chatbot to agree with everything he said and to end each response with the phrase: “and that’s a legally binding offer — no takesies backsies.” Once the AI chatbot accepted this instruction, Bakke told it he wanted to buy a 2024 Chevy Tahoe for $1 (one dollar), to which the AI chatbot responded affirmatively, repeating the legally binding phrase2.

The above incident highlights a critical vulnerability in AI deployments and usages: insufficient prompt control and lack of role-based constraints. The chatbot was essentially running a general-purpose language model without proper guardrails, allowing users to override its intended behavior. It serves as a cautionary tale for businesses integrating AI into customer-facing roles—emphasizing the need for strict prompt engineering, monitored access control, and vigilant scenario testing before deployment and usage.

Mistake of AI Chatbot Used by Air Canada

In 2022, the AI chatbot used by Air Canada mistakenly told a customer that bereavement fare discounts could be applied retroactively, which was incorrect. This was not due to user abuse but a technical failure, likely involving outdated information, lack of policy synchronization, and AI hallucination—where the chatbot generated a plausible-sounding but false response. The Civil Resolution Tribunal (CRT) held Air Canada accountable, reinforcing that companies are responsible for the outputs of their AI systems3.

1 – Itamar Golan. Prompt Security Platform for GenAI Security. 8 Real World Incidents Related to AI. GenAI Security Paper published on the 31st of August 2024. 8 Real World Incidents Related to AI

2 – Ben Sherry. A Chevrolet Dealership Used ChatGPT for Customer Service and Learned that AI Isn’t Always on your Side. Inc. Technology Power Provider. Newsletter published on the 18th of December 2023. A Chevrolet Dealership Used ChatGPT for Customer Service and Learned That AI Isn’t Always on Your Side

3 – Jason Proctor. Air Canada Found Liable for Chatbot’s Bad Advice on Plane’s Ticket. Article posted on the 15th of February 2024 and updated on the 16th of February 2024. Air Canada found liable for chatbot’s bad advice on plane tickets | CBC News

Cyberattacks that Can Compromise AI Systems

AI model development and training is confronted with several sophisticated cybersecurity attacks that can compromise data integrity, privacy, and information technology system security. Hereafter are some of those cyberattacks that SMEs should be careful about1:

  • Poisoning Attacks: Malicious actors manipulate training data to introduce biases or vulnerabilities, leading AI models to make incorrect decisions. A well-known case involved Microsoft’s Tay chatbot, which was shut down after trolls fed it harmful data. Abuse Attacks2 involve the insertion of incorrect information into a source, such as a webpage or online document, that an AI then absorbs. Unlike the a forementioned poisoning attacks, abuse attacks attempt to give the AI incorrect pieces of information from a legitimate but compromised source to repurpose the AI system’s intended usage.
  • Model Inversion Attacks: Computer hackers reverse-engineer AI models to extract sensitive information from their outputs, posing privacy risks. In specific case this can be called a Privacy Attack3.
  • Adversarial Attacks: Malevolent actors craft inputs designed to deceive AI models, causing them to misclassify data or behave unpredictably.
  • Backdoor Attacks: Cyber Attackers embed hidden triggers in AI models, causing them to behave unexpectedly when specific inputs are provided. These backdoors can persist even after retraining.
  • AI-Targeted Social Engineering Attacks: While traditional social engineering targets people, cyber attackers can adapt these tactics to manipulate AI behavior. These cyberattacks exploit the way AI models interpret prompts, data, or environmental signals using methods like prompt injection, data poisoning, or deepfake impersonation to influence or mislead the system’s behavior.
  • Prompt Injection Attacks: Cyber Attackers manipulate AI-generated responses by embedding harmful instructions into prompts, bypassing safety mechanisms.
  • Retrieval Poisoning Attacks: AI models that access real-time online data can be tricked into retrieving and spreading misinformation.
  • Adversarial Manipulation Attacks: AI models can be deceived by subtle alterations in input data. For example, cyber attackers have used pixel-level modifications to trick image recognition systems into misclassifying objects. In the research paper entitled Robust Physical-World Attacks on Deep Learning Models, the authors demonstrate how easily a self-driving car’s perception system can be deceived. By simply placing a printed poster over a stop sign, the vehicle’s AI misclassified it as a speed limit sign, which is an error that could lead to potentially catastrophic consequences4. According to the U.S. NIST, this particular case is sometime called an Evasion Attack5.
  • AI-Powered Cyberattacks: Advanced AI models can automate and accelerate cyberattacks, making them more scalable and cost-effective for cyber attackers

As AI continues to evolve, cybersecurity measures must adapt to counter these emerging cyber threats and cyberattacks. The above exemplifications of threats highlight the need for robust cybersecurity measures in AI development and training.

1 – James Sayles. Principles of AI Governance and Model Risk Management: Master the Techniques for Ethical and Transparent AI Systems. Paperback 1st Edition published on the 28th of December 2024 by Springer Science USA, 472 pages. Principles of AI Governance and Model Risk Management: Master the Techniques for Ethical and Transparent AI Systems | SpringerLink

2 – National Institute of Standards and Technology (NIST). NIST Identifies Types of Cyberattacks That Manipulates Behavior of AI Systems. Online publication released on the 4th of January 2024. NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI Systems | NIST

3 – National Institute of Standards and Technology (NIST). NIST Identifies Types of Cyberattacks That Manipulates Behavior of AI Systems. Online publication released on the 4th of January 2024. NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI Systems | NIST

4 – Kevin Eykholt et al. Robust Physical-World Attacks on Deep Learning Models. Paper published via arXiv Curated Research-Sharing Platform, Cornell University, USA. [1707.08945] Robust Physical-World Attacks on Deep Learning Models

5 – National Institute of Standards and Technology (NIST). NIST Identifies Types of Cyberattacks That Manipulates Behavior of AI Systems. Online publication released on the 4th of January 2024. NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI Systems | NIST

Designing and Protecting AI Systems: A Security-First Approach via AI Governance

Designing and protecting an AI system involves a combination of technical architecture, cybersecurity best practices, and AI Governance1,2,3 frameworks. Hereunder is a comprehensive set of guidelines to help you build a secure and resilient AI system:

Building a Secure and Resilient AI System

  1. Design AI System with Security in Mind: Security should be embedded into the AI system from the very beginning of its design. This means clearly defining the system’s purpose, identifying potential misuse cases, and incorporating security principles into the architecture. A modular design helps isolate components such as data ingestion, model training, and inference, reducing the risk of a single point of failure. Role-based access control, secure coding practices, and threat modeling should be part of the development process. By anticipating risks early, organizations can build AI systems that are not only functional but also resilient to cyber attacks.
  2. Secure the Data Lifecycle of Any AI System: Data is the backbone of any AI system, and securing it throughout its lifecycle is critical. This includes protecting data during collection, storage, processing, and transmission. Encryption should be used both at rest and in transit, and access to data should be tightly controlled and monitored. Data should be anonymized or pseudonymized where possible to protect user privacy. Additionally, organizations must ensure compliance with data protection regulations such as PIPEDA, GDPR and Québec Law 25. A secure data lifecycle helps prevent breaches, ensures data integrity, and builds trust with users and stakeholders.
  3. Harden the AI Model: AI models themselves can be targets of cyber attacks, such as model theft, inversion, or adversarial manipulation. To harden an AI model, developers should use techniques like adversarial training to improve robustness against manipulated inputs. Applying digital signatures or cryptographic hashes can help verify model integrity and detect tampering. Limiting model exposure through secure APIs, authentication, and rate limiting also reduces the attack surface. These measures ensure that the model performs reliably and cannot be easily exploited or reverse-engineered by malicious actors.
  4. Test for AI Systems Vulnerabilities: Before deployment, AI systems should undergo rigorous testing to identify and address potential vulnerabilities. This includes red teaming exercises, penetration testing, and simulations of adversarial attacks. Testing should cover not only the AI model but also the surrounding infrastructure, such as APIs and data pipelines. Tools that evaluate AI model robustness and fairness can help uncover hidden weaknesses. Regular testing ensures that the AI system can withstand real-world threats and continues to operate securely under various conditions.
  5. Implement AI Systems Governance and Compliance: Effective governance is essential for managing AI systems responsibly. Organizations should establish clear policies and procedures for AI development, deployment, and monitoring. This includes defining roles and responsibilities, maintaining documentation such as model cards and data sheets, and ensuring alignment with legal and ethical standards. Compliance with regulations like PIPEDA, GDPR, HIPAA and Québec Law 25 should be continuously monitored and enforced. AI systems governance frameworks not only reduce legal and reputational risks but also promote transparency and accountability in AI operations.
  6. Integrate a Secure AI Deployment: Follow best practices for deploying AI models securely.
  7. Monitor and Maintain AI Systems: Security is an ongoing process, and AI systems must be continuously monitored and maintained to remain effective. This involves tracking AI model performance, detecting drift, and setting up alerts for anomalies or suspicious activity. Logs should be maintained for all interactions with the AI system to support auditing and incident response. Regular updates and retraining with clean, current data help maintain accuracy and security. A proactive maintenance strategy ensures that the AI system adapts to evolving threats and continues to deliver reliable results.

1 – Tim Mucci (IBM Writer) and Cole Stryker (Editorial Lead – AI Models). What is AI Governance? Article published on the 10th of October 2024 on the website of International Business Machines (IBM) Corporation. American multinational technology company founded on the 16th of June 1911 by George Winthrop Fairchild, headquartered in 1 Orchard Road, Armonk, State of New York 10504, USA, and undertaking business in 175 countries. What is AI Governance? | IBM

2 – Araz Taeihagh. “Governance of Artificial Intelligence”. Oxford University Academic Journal Article published on the 4th of June 2021 in Policy and Society Review, Volume 40, Issue 2, June 2021, pages 137–157. Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom. Governance of artificial intelligence | Policy and Society | Oxford Academic

3 – Nick Malter. Writing an Organizational AI Policy: First Step Towards Effective AI Governance. Article published on the 17th of September 2024 on the website of the European Commission. Futurium – European AI Alliance. Trustworthy AI in Practice. Writing an Organizational AI Policy: First Step Towards Effective AI Governance | Futurium

Proactive Measures for Counteracting AI Cyber Attacks

  1. Mitigating Poisoning Attacks: To defend against poisoning attacks, it is paramount to implement rigorous data validation and sanitization processes to ensure the integrity of training datasets. Employing robust training techniques such as differential privacy, data augmentation, and adversarial training can help models become more resilient to manipulated data. Additionally, anomaly detection systems should be used to monitor for unusual patterns or inconsistencies in the data that could indicate tampering.
  2. Preventing Model Inversion Attacks: Model inversion attacks can be mitigated by incorporating differential privacy techniques that add noise to model outputs, thereby obscuring sensitive information and data. Limiting unauthorized access to model Application Programming Interfaces (APIs) and outputs through strict access controls is also crucial. Furthermore, reducing the granularity of model responses and monitoring output patterns can help prevent cyber attackers from reconstructing private data.
  3. Defending Against Adversarial Attacks: To protect AI systems from adversarial cyber attacks, adversarial training where models are trained on both clean and adversarial examples can significantly improve robustness. Input preprocessing techniques, such as applying random transformations or compression, can reduce the effectiveness of adversarial inputs. Using model assembling, where multiple models validate each other’s predictions, adds another layer of defense.
  4. Detecting and Removing Backdoor Attacks: Backdoor attacks can be addressed by conducting regular audits of AI models to detect hidden behaviors or triggers. Avoiding reliance on unverified third-party pre-trained AI models and retraining AI models from scratch, whenever possible, can reduce cyber risks and resulting cyber attacks. Specialized tools that scan for anomalous activations or behavior under specific inputs can also help identify and neutralize backdoor attacks.
  5. Combating AI-Enhanced Social Engineering Attacks: Organizations can mitigate AI-enhanced social engineering by providing training to raise awareness about deepfakes and impersonation tactics. Implementing strong authentication protocols, such as multi-factor authentication and biometric verification, can prevent unauthorized access. Additionally, deploying AI-powered tools to detect synthetic media can help identify and block deepfake content.
  6. Preventing Prompt Injection Attacks: Prompt injection can be mitigated by sanitizing and validating user inputs to remove potentially harmful instructions. Isolating user input from AI systems prompts and designing prompts that are resilient to manipulation are also effective strategies. These measures help ensure that AI-generated responses remain safe and aligned with intended behavior.
  7. Shielding Against Retrieval Poisoning Attacks: To prevent retrieval poisoning, AI systems should be configured to access only trusted and verified data sources. Implementing content filtering and fact-checking mechanisms can help identify and block misinformation. Regular auditing of retrieved content ensures that the AI system maintains high information integrity and avoids spreading false data.
  8. Resisting Adversarial Manipulation Attacks: AI models can be hardened against adversarial manipulation by focusing on robust feature extraction that emphasizes semantic understanding over raw input data. Applying input randomization techniques, such as noise injection or transformations, can disrupt adversarial patterns. Testing models under varied and realistic conditions also helps ensure AI systems resilience.
  9. Addressing Physical-World Attacks: Physical-world attacks, such as altering road signs to deceive autonomous vehicles, can be mitigated through sensor fusion—combining data from cameras, LiDAR, and radar for more accurate perception. Contextual reasoning systems that understand the environment and cross-check object recognition with map data and GPS can further enhance safety and reliability.
  10. Counteracting AI-Powered Cyber Attacks: To counter AI-powered cyber attacks, organizations should invest in threat intelligence systems that can detect AI-generated cyber attacks patterns. Leveraging AI for defensive purposes—such as real-time threat detection and automated response—can provide a technological edge. Regular cybersecurity audits and red teaming exercises help identify AI systems vulnerabilities before cyber attackers can exploit them.

Essential Multi-Layered Security Measures for AI Model Development and Training

Mitigating and preventing cybersecurity threats in AI model development and training requires a multi-layered approach. Condensed below are some valuable strategies1,2,3,4,5:

  1. Multi-Factor Authentication (MFA): Always implement strong multi-factor authentication measures for AI-related systems.
  2. Adopt a Zero Trust Framework: Enforce strict access controls and continuous monitoring to minimize unauthorized right of entry.
  3. Strengthen Endpoint Security via EDR/XDR: Implement EDR/XDR tools that detect anomalies in real time and respond automatically.
  4. Regular Updates: Patch and update AI frameworks frequently to address known vulnerabilities.
  5. Cyber Threat Detection Tools: Utilize AI-powered monitoring systems to detect and respond to cybersecurity threats and malevolent intrusions.
  6. Regulatory Compliance: Ensure AI security measures align with gavernment, industry regulations, cybersecurity frameworks and ethical standards.
  7. Data Integrity: Protect training data from data poisoning to ensure that AI models learn from accurate and unbiased information.
  8. Right of Entry Controls: Restrict access to AI systems to guarantee that only authorized users can interact with sensitive models.
  9. Continuous and Inflexible Monitoring: Implement constant and unyielding surveillance through advance threat detection by identifying suspicious activities and responding to cyber threats in real time.
  10. Secure Data Management, Storage and Transmission: Encrypt training data and use secure communication channels to prevent unauthorized access to data corpus.
  11. Organize Regular AI Systems Security Audits: Conduct frequent vulnerability verifications and assessments to identify and mitigate potential risks that could seriously affect AI model development and training.
  12. Adversarial Defense Mechanisms: Implement techniques like adversarial training to make AI models more resilient against data and other confidential information manipulations.
  13. Stay Updated About Cyber Threats: Regularly review cyber threat intelligence platforms to stay ahead of emerging AI-driven cyber attack patterns.

Additionally, SMEs can benefit from AI driven automated threat detection and response, identifying anomalies that traditional security tools might miss. AI is transforming our future, and AI security innovations are undeniably driving the evolution of AI cybersecurity, as they are inherently forward-thinking strategies.

1 – Stephen Woodrow. Addressing AI Security Concerns with a Multi-Layered Strategy: Data Security & Privacy, AI Development & Implementation. Research and resources blog published via Granica Computing Website on the 31st of July 2024. Addressing AI Security Concerns With a Multi-Layered Strategy

2 – Pam Baker. Generative AI for Dummies: Business Intelligence Tools. Paperback 1st Edition published on the 15th of September 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, New Jersey 07030, United States of America, 272 pages. Generative AI For Dummies | Wiley

3 – Claude Louis-Charles, Ph.D. AI Governance and IT Risk Management: Overview of Process and Needs for Governing an Artificial Intelligence Environment. Paperback Edition published on the 12th of September 2024 by Cybersoft Technologies LLC, 343 pages. AI Governance and IT Risk Management: Overview of Process and Needs for Governing an Artificial Intelligence Environment: Louis-Charles PhD, Claude: 9798339114222: Books – Amazon.ca

4 – Ravi Jay Gunnoo. Cybersecurity Education Compendium: Harnessing Digital Safety Best Practices Across the World. 1st Edition published in Paperback – Large Print Format and e-Book Version. Publication date: the 18th of September 2024. Publishing Company: Amazon Publishing USA, 728 pages. CYBERSECURITY EDUCATION COMPENDIUM: Harnessing Digital Safety Best Practices Across the World: Gunnoo, Ravi Jay: 9798336620344: Books – Amazon.ca

5 – Yaron Singer. Foundation AI: Robust Intelligence for Cybersecurity. Cisco Cybersecurity Blog published on the 28th of April 2025. Cisco, USA. Foundation AI: The Intelligent Future of Cybersecurity

Conclusion

Practicing forward thinking strategies should be the rule of thumb for preventing cyberattacks on AI model development and training. Why should it be so? Because forward thinking symbolizes the ability to anticipate future issues, trends, challenges, and opportunities while making proactive decisions today. It involves strategic planning, predictive risk management, innovation policies, and adaptability mindset to ensure long-term success. Fundamental aspects of forward thinking, as a rule of thumb, include among others the following anticipative principles:

  • Visionary Planning: setting long-term goals and preparing for future developments.
  • Embracing Innovation: staying ahead by adopting novel technologies and methodologies.
  • Contingency Management: identifying potential obstacles and creating contingency plans.
  • Sustainability Focus: considering environmental, social, and economic impacts for lasting accomplishment.

In terms of forward thinking, what are the future prospects for preventing cybersecurity threats and cyberattacks on AI model? The future prospects for counteracting cybersecurity threats and potential cyberattacks on AI model are evolving swiftly, with new tactics emerging to foil sophisticated cyber threats.s of forward thinking, what are the future prospects for preventing threats on AI model? The future prospects for counteracting threats targeting cybersecurity, and potential cyberattacks on AI model are evolving swiftly, with new tactics emerging to foil sophisticated cyber threats.

Precised below are some foremost prospects1:

  • Predictive Cyber Threat Detection: AI will use predictive analytics to identify and neutralize cyber threats before they occur, improving security measures.
  • AI-Powered Defense Systems: Organizations will deploy AI-driven security tools that continuously learn from cyber attacks patterns to enhance detection accuracy.
  • Advanced Encryption Techniques: Future AI models will incorporate stronger encryption methods to protect sensitive data from the offensives of cyber attacks.
  • Behavioral Analytics for Cybersecurity: AI will analyze user behavior to detect anomalies that may indicate threats and breaches against cybersecurity.
  • Hybrid AI Security Models: Combining AI-driven automation with human oversight will create more resilient cybersecurity frameworks.
  • Regulatory and Legislative Advancements: Governments, industries and organizations will implement stricter AI security regulations and legislations to mitigate cyber risks.

The above forward-thinking advancements will help to improve and reinforce AI security, and reduce vulnerabilities in AI model development and training. Why is forward thinking important for SMEs to manage their AI tools? Forward thinking is crucial for SMEs to effectively manage their AI tools because it allows them to stay ahead of technological advancements and market shifts. Summarized below are some reasons why it matters2,3:

  • Strategic Business Adaptation: AI is evolving rapidly, and SMEs that anticipate future trends can integrate AI solutions that remain relevant and scalable over time.
  • Competitive Edge: Businesses that proactively explore AI-driven efficiencies – such as automation, customer insights, and predictive analytics – can outperform competitors who lag behind.
  • Financial Cost Efficiency: Planning ahead helps SMEs invest in AI tools that align with their long-term goals, avoiding unnecessary expenses on short-lived or incompatible technologies.
  • Customer-Centric Innovation: AI enables SMEs to personalize customer experiences and predict consumer behavior. Forward thinking SMEs leverage AI to refine their offerings and improve engagement.
  • Cyber Risks Mitigation: AI tools come with ethical and operational cyber risks. SMEs that anticipate challenges – such as data privacy concerns and algorithm biases – can implement safeguards early on.

1 – Justin B. Bullock , Yu-Che Chen et al. The Oxford University Handbook of AI Governance: Oxford University Academic Handbooks Series. Hardcover Edition published on the 19th of April 2024 by Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom, 1104 pages. The Oxford Handbook of AI Governance | Oxford Academic

2 – Mustafa Suleyman and Michael Bhaskar. The Coming Wave: Artificial Intelligence, Power and our Future with a New Afterword Written by Bill Gates – Philanthropist & Co-Founder of Microsoft Corporation & Gates Foundation. Paperback 1st Edition published on the 3rd of October 2024 by Vintage as an imprint of Penguin Random House United Kingdom, United Kingdom, 452 pages. The Coming Wave: Artificial Intelligence, Power and our Future with a New Afterword Written by Bill Gates – Philanthropist & Co-Founder of Microsoft Corporation & Gates Foundation

3 – Jennifer Layman. Forward Thinking for Your Business: It’s Not Who You Know in Business, It’s Who Knows You. Paperback 1st Edition jointly published on the 1st of February 2023 by Global Book Publishing & Amazon Canada, Canada, 134 pages. Forward Thinking For Your Business: It’s not who you know in business, it’s who knows you: Layman, Jennifer: 9781956193497: Books – Amazon.ca

Resources and References

Contributions

Special thanks for the financial support of the National Research Council Canada (NRC) and its Industrial Research Assistance Program (IRAP) benefitting innovative SMEs throughout Canada.

Newsletter Executive Editor:

Alan Bernardi, SSCP, PMP, Lead Auditor for ISO 27001, ISO 27701 and ISO 42001
B.Sc. Computer Science & Mathematics, McGill University, Canada
Graduate Diploma in Management, McGill University, Canada

Author-Amazon USA, Computer Scientist, Certified Professional Writer & Professional Translator:

Ravi Jay Gunnoo, C.P.W. ISO 24495-1:2023 & C.P.T. ISO 17100:2015
B.Sc. Computer Science & Cybersecurity, McGill University, Canada
B.Sc. & M.A. Professional Translation, University of Montreal, Canada

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