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Why Your Cybersecurity Team Should Lead AI Governance and Set Up an AIMS The rapid rise of artificial intelligence is reshaping industries, and its benefits are undeniable. But with great power comes great responsibility. As AI systems increasingly make decisions that impact operations, security, and even ethics, it’s clear that organizations need more than innovation—they need governance. Enter the cybersecurity team. Cybersecurity professionals already understand the intersection of technology, risk, and compliance better than most. We’ve spent years identifying vulnerabilities, managing threats, and building frameworks to secure complex systems. These same principles are essential for AI governance, which is why cybersecurity should take the lead. Why Cyber Teams Are Perfect for AI Governance: 1. Risk Management Expertise: AI systems are prone to risks like data poisoning, adversarial attacks, and algorithmic biases. Cyber teams are trained to anticipate and mitigate such threats. 2. Regulatory Knowledge: With the AI Act now in force (and similar regulations on the horizon globally), compliance is critical. Cyber teams are already adept at aligning technology practices with laws and standards. 3. Ethical Oversight: Much like protecting personal data, ensuring AI operates ethically requires clear boundaries and monitoring. Cybersecurity brings a culture of accountability that can be extended to AI systems. The Case for an AI Management System (AIMS): An AI Management System (AIMS) is the AI equivalent of an ISMS (Information Security Management System). It’s a structured approach to governing AI across the organization, ensuring risks are managed, compliance is maintained, and performance aligns with strategic goals. AIMS enables: ? AI Risk Assessments: Identifying vulnerabilities in data, algorithms, and decision-making processes. ? Incident Response for AI Failures: Preparing for when AI systems malfunction or make biased decisions. ? Continuous Monitoring: Ensuring AI systems remain secure, fair, and transparent throughout their lifecycle. ? Cross-Functional Collaboration: Bringing together stakeholders from IT, legal, and business to address AI challenges holistically. By setting up an AIMS, your organization doesn’t just stay compliant—it stays ahead. And who better to lead this effort than the team already dedicated to protecting your organization’s digital backbone? Cybersecurity isn’t just about protecting data; it’s about safeguarding trust. Let’s extend that trust to AI. At NS we walk this talk! #Cybersecurity #AI #Governance #AIMS #Leadership #AIRegulation #RiskManagement

?? Introducing Guardium AI Security: Protecting AI, Data, and Business ?? ?? As AI adoption accelerates, organizations face critical challenges: - Hidden AI Risks: Shadow AI deployments are operating without visibility, leaving security teams unaware of their connections to sensitive data and applications. - Weak Security Posture: Vulnerabilities like account takeovers, misconfigurations, and excessive permissions expose AI systems to breaches and attacks such as prompt injection. - Complex Compliance Requirements: Evolving regulations demand strict oversight of AI deployments and data usage, with hefty fines for non-compliance. ???? AI innovation is a game-changer—but without the right safeguards, it can lead to: - Data Breaches: The interaction between models, data, and apps can create unforeseen vulnerabilities. - Operational Disruption: Compromised AI systems can jeopardize critical processes. - Regulatory Penalties: Non-compliance with global data privacy and AI governance frameworks can result in financial and reputational damage. ??IBM created Guardium AI Security to empower organizations to confidently secure their AI deployments: - Unparalleled Visibility: Discover and inventory all AI models, including shadow AI, across multi-cloud and multi-vendor environments. - Proactive Risk Management: Automated risk scoring prioritizes vulnerabilities, with actionable recommendations to mitigate threats. - OWASP Top 10 Alignment: Follow industry-leading frameworks to protect your AI systems from emerging attack vectors. - Integrated Compliance: Seamlessly align with global regulations using tools like watsonx.governance for a holistic view of security and business risk. With Guardium AI Security, organizations can innovate with confidence, secure sensitive data, and stay ahead of evolving threats and regulations. Ready to safeguard your AI-driven future? Let’s connect! #AI #CyberSecurity #GuardiumAI #DataProtection #Innovation #Compliance

?? ???????????????? ???????????????????????? ???? ???????????????? ????????????????????: ???? ?????? ?????????????? ???????????????? ???? ?????? ?????????????????? Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of transforming the cybersecurity industry, offering new capabilities and reshaping how security consultants approach their strategies. Here's a closer look at how these technologies are being integrated into security practices and the profound implications for consultants: Predictive Capabilities: AI and ML algorithms are being utilized to predict and preemptively identify potential threats by analyzing patterns and anomalies in vast amounts of data. This allows consultants to offer proactive solutions that can prevent breaches before they occur. Enhanced Threat Detection: With the integration of AI, security systems can now detect and respond to threats more efficiently. Machine learning models continuously learn from new data, improving their detection capabilities over time and reducing false positives, thereby enhancing overall security posture. Automated Incident Response: AI technologies enable faster response to security incidents. Automated systems can instantly react to and mitigate threats, reducing the need for manual intervention and allowing consultants to focus on strategic, high-level security concerns. Sophisticated Risk Assessment: AI and ML contribute to more sophisticated risk assessment models. Consultants can use these technologies to analyze and quantify risk more accurately, providing tailored advice to clients based on predictive analytics and trend analysis. Scaling Security Operations: As organizations grow, manually managing security becomes impractical. AI and ML allow for the scaling of security operations efficiently and effectively, accommodating the increasing complexity and volume of threats without compromising on response time or quality of protection. ------------------------------------ Are you ready to dive into the future of cybersecurity consulting with AI and ML? Whether you're looking to enhance your security strategy or you are a consultant eager to leverage these technologies, let's connect and discuss how we can transform your security landscape. #SecurityConsulting #EmergingTech #AIinCybersecurity #MachineLearning #CybersecurityInnovation

?? ???????????? ???? '???????????? ????': ?? ?????????????? ?????????????? ???? ?????? ???????? ?????????? ???? The rapid evolution of AI is an incredible opportunity—but it also brings hidden risks. One such concern is 'Shadow AI', a term used to describe unregulated and unsupervised AI systems operating under the radar. ?? What’s at stake? From data breaches ?? and compliance violations ?? to inaccurate outputs ??, the risks associated with Shadow AI could lead to catastrophic consequences if left unchecked. ?? How can we address this? Here are proactive measures your organization can take to stay ahead: 1?? Identify Risks: Understand Shadow AI threats, such as security vulnerabilities and legal/Compliance implications. 2?? Governance First: Establish clear AI governance frameworks within your organization, outlining acceptable use policies, data security protocols, and compliance standards.? 3?? Educate Your Team: Implement employee training programs to educate staff about the risks of Shadow AI and the importance of responsible AI usage. ?????? 4?? Monitor Activity: Use monitoring tools to track AI usage, data flow, and restrict access to sensitive information to prevent unauthorized access.???. 5?? Audit Regularly: Conduct regular audits of AI tools and their outputs to ensure compliance with internal policies and external regulations. ???. ?? As we continue leveraging AI for innovation, let’s also build safeguards to prevent it from becoming a shadowy threat. What are your thoughts on Shadow AI and how organizations can mitigate its risks? ?? Share your ideas below! ?? #AI #Innovation #Cybersecurity #ShadowAI #Technology #Governance

??? ???????????????????????? ???????????????? ???????????????????????????? ???? ?????? ?????? ???? ????: ????????’?? ?????????????????????????? ???????????????????? ?? Artificial intelligence (AI) is transforming critical infrastructure by enhancing efficiency and predictive capabilities. Yet, this progress introduces significant risks, including AI-driven cyberattacks and systemic vulnerabilities. Recognizing these challenges, the Cybersecurity and Infrastructure Security Agency (CISA) has released comprehensive guidelines rooted in the NIST AI Risk Management Framework. These measures aim to help sectors embrace AI innovation while safeguarding public safety and critical services from disruption. ?? CISA’s guidelines emphasize a lifecycle-based framework: Govern, Map, Measure, and Manage. Key priorities include securing AI systems against adversarial manipulation, preventing design failures, and enhancing resilience through human oversight and validation. The framework also highlights data integrity, mitigation of AI-enabled social engineering, and addressing emerging risks like generative AI misuse. By fostering partnerships among AI vendors, industry leaders, and regulators, the guidelines create a united front against evolving threats. ?? As a cybersecurity professional, I find these guidelines transformative and essential. The reliance on AI in critical infrastructure necessitates a culture of "security-first innovation." Challenges like inscrutability, bias, and overreliance on AI demand a human-centric approach that combines technical safeguards with workforce training, transparent AI practices, and active threat modeling. Measures like adversarial testing, vendor accountability, and rigorous validation are non-negotiable to prevent catastrophic failures. By embedding these principles, we can unlock AI’s potential while ensuring its safe integration into vital systems. ?? How do you see AI transforming risk management in critical infrastructure? What steps are you taking to align AI innovation with robust cybersecurity practices? https://lnkd.in/gfaYknhQ #artificialintelligence #criticalinfrastructure #cybersecurityinai #cybersecurity #cyberriskmanagement

Day 12/31: Do you think the rise of artificial intelligence will reduce the need for cybersecurity professionals? The rise of artificial intelligence (AI) is changing the cybersecurity landscape, but it's unlikely to reduce the need for cybersecurity professionals—if anything, it will enhance their roles. While AI can automate many repetitive tasks like threat detection, monitoring, and vulnerability scanning, it won’t replace the need for skilled experts. AI will serve as a tool that empowers professionals to focus on complex decision-making, strategy, and incident response. However, as AI advances, so do cyber threats. Attackers will leverage AI to create more sophisticated hacking techniques, requiring skilled professionals to stay ahead of these new attack vectors. Cybersecurity professionals will still play a critical role, as human judgment, ethics, and nuanced decision-making are essential in managing and interpreting AI-driven systems. AI might be smart, but it can’t replicate human instincts when it comes to ethical decisions or risk management. Additionally, with more organizations adopting AI, there will be an increased need for experts who can oversee the governance and security of AI systems. Managing AI risks and ensuring the systems are secure, ethical, and unbiased will require cybersecurity professionals with deep expertise in both fields. In fact, the rise of AI will likely increase the demand for cybersecurity professionals who are skilled in AI and machine learning. Rather than reducing the need for talent, AI is shifting the skills required in cybersecurity. AI may change the way we approach cybersecurity, but human expertise will always be the cornerstone of effective defense strategies. The future of cybersecurity will be a partnership between AI and professionals, not a replacement. Shout out to Dr Iretioluwa Akerele for this #31DaysChallenege #CybersecurityAwarenessMonth #SecureYourWorld #Cybersecurity #AI #ArtificialIntelligence #FutureOfWork #Automation

As organizations increasingly integrate Artificial Intelligence (AI) into their business operations, it is crucial to implement policies that address both the challenges and opportunities of AI. These policies should ensure that AI outputs are critically evaluated, safeguarding against potential errors and biases. Additionally, they must provide comprehensive governance to protect the organization’s data from cyber threats and establish clear guidelines for the ethical and responsible use of AI across all aspects of the business.??This is why it is important for organizations to adopt an AI policy sooner than later. An AI policy will provide guidelines for the implementation, management, and governance of AI within an organization to ensure ethical, secure, and efficient use of AI technologies. The policy should apply to all departments, employees, contractors, and third-party entities involved in the development, deployment, and utilization of AI systems within your organization. At minimum, the following areas should be covered and established as guidelines for a comprehensive AI policy: 1. Ethical Use of AI 2. Data Privacy and Security 3. Governance and Accountability 4. Training and Awareness 5. Risk Management 6. Compliance and Legal Considerations 7. Monitoring and Evaluation 8. Innovation and Continuous Improvement The IT department, in collaboration with the AI governance committee, should be responsible for implementing the policy. Regular reviews and audits should be conducted to ensure compliance and address any emerging issues. The policy should also be reviewed annually and updated as necessary to ensure it remains relevant and effective. The graphic below shows a basic framework for developing an AI policy for an organization. #RECC #commercialrealestate #cybersecurity #cyberharmony #builtenvironment #artificialintellengence #AI

Organisations are beginning to implement artificial intelligence (AI) solutions at scale and the enterprise software they use is increasingly AI powered. The aim is to increase efficiency, productivity and creativity, but the technology brings significant additional cyber risks. As we adopt AI at speed Cyber and Risk Leaders need to focus of the following three aspect of AI enablement at speed. 1?? adopt a three stranded strategy covering technology, governance and operations to address new risks. 2?? human error and poor adoption of best practices represent the biggest single cyber risk in relation to AI adoption. 3?? all aspects of the data management lifecycle must be viewed through a cybersecurity lens. For a more indepth conversation connect with our EY Ireland leaders to support your journey to an AI ready workforce. Barry McCarthy Niamh O'Beirne Tom Slattery John Ward Eoin O'Reilly Paul Pierotti Carol Murphy Megan Conway Tim Bergin Ivan O'Brien Hugh Callaghan Diarmuid Curtin Jason Guy Laura Flynn Paul Browne Richard Watson Piotr Ciepiela #cyber #ai #workforce #futureready #transform #risk

Leveraging AI for cyber operations 3 ways to use —3 risks to manage? I have been engaged with the use of machine learning and AI since as far back as 2014 in the private sector. Meanwhile, elements within the US government have developed substantial expertise in actual AI technologies over the past 15 years. This illustrates not only the longevity but also the expanding utilization of AI across various domains. It is important to recognize, however, that some companies may exploit AI as a marketing tool to capture the attention of CISOs—this tactic is often quite transparent, and unfortunate. Given this landscape, what should CISOs and their teams be vigilant about?? Here are 6 things I manage and look out for: Implement Threat Detection and Response Systems powered by generative AI. Deploy AI algorithms that can analyze big datasets at the nanoseconds order and are much more capable of enhancing your ability to detect and react to cyber threats faster and more efficiently. Critical for this quick response capability in a landscape where every second is essential. Leverage predictive analytics and risk assessment: Enterprise AI should use forecasting trends and tendencies to forecast threats and vulnerabilities to security. An ability that is this proactive will allow you to mitigate risks even before they blossom into serious cyber incidents, hence offering you more security. AI-driven Automation: Improve your incident response processes with AI-driven automation to make them faster. It helps accelerate your response times and dramatically enhances the efficiency of your security operations to exercise a level of control and limitation on a breach never obtained before. But with such high benefits involved in integrating enterprise AI into your cybersecurity strategy, some downsides must also accompany these rewards: Data Privacy Concerns: Since AI systems handle sensitive information, even this needs to be fortified about the breach, compromising data privacy.? Algorithmic Bias: Your AI algorithms may have biases that lead to discriminatory outcomes, which would seriously compromise your security operations' soundness.? Dependence on AI: Even if AI solutions can help you boost your security operations almost perfectly, always bear in mind that in all their jobs, there is a critical need for human oversight. #CISO #CIO #CEO #Cybersecurity #Cyber&AI #AI Jason Firch, MBA Joshua Copeland Walter Haydock Allan Alford