AI and Cybersecurity Measures

Yesterday, the National Security Agency Artificial Intelligence Security Center published the joint Cybersecurity Information Sheet Deploying AI Systems Securely in collaboration with the Cybersecurity and Infrastructure Security Agency, the Federal Bureau of Investigation (FBI), the Australian Signals Directorate’s Australian Cyber Security Centre, the Canadian Centre for Cyber Security, the New Zealand National Cyber Security Centre, and the United Kingdom’s National Cyber Security Centre. Deploying AI securely demands a strategy that tackles AI-specific and traditional IT vulnerabilities, especially in high-risk environments like on-premises or private clouds. Authored by international security experts, the guidelines stress the need for ongoing updates and tailored mitigation strategies to meet unique organizational needs. 🔒 Secure Deployment Environment: * Establish robust IT infrastructure. * Align governance with organizational standards. * Use threat models to enhance security. 🏗️ Robust Architecture: * Protect AI-IT interfaces. * Guard against data poisoning. * Implement Zero Trust architectures. 🔧 Hardened Configurations: * Apply sandboxing and secure settings. * Regularly update hardware and software. 🛡️ Network Protection: * Anticipate breaches; focus on detection and quick response. * Use advanced cybersecurity solutions. 🔍 AI System Protection: * Regularly validate and test AI models. * Encrypt and control access to AI data. 👮 Operation and Maintenance: * Enforce strict access controls. * Continuously educate users and monitor systems. 🔄 Updates and Testing: * Conduct security audits and penetration tests. * Regularly update systems to address new threats. 🚨 Emergency Preparedness: * Develop disaster recovery plans and immutable backups. 🔐 API Security: * Secure exposed APIs with strong authentication and encryption. This framework helps reduce risks and protect sensitive data, ensuring the success and security of AI systems in a dynamic digital ecosystem. #cybersecurity #CISO #leadership

Today, NIST released the initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile), a community profile built on NIST CSF 2.0 to help organizations manage cybersecurity risk in an AI-driven world. A key section of this draft is Section 2.1, which introduces three Focus Areas that explain how AI and cybersecurity intersect in practice: 1. Securing AI System Components (Secure) AI systems introduce new assets that must be secured; models, training data, prompts, agents, pipelines, and deployment environments. This focus area emphasizes treating AI components as first-class cybersecurity assets, integrating them into governance, risk assessments, protection controls, and monitoring processes. It reinforces that AI risk should not be siloed from enterprise cybersecurity risk management. 2. Conducting AI-Enabled Cyber Defense (Defend) AI is not just something to protect, it is also a powerful defensive capability. This area focuses on using AI to enhance detection, analytics, automation, and response across security operations. At the same time, it recognizes the risks of over-reliance on automation, model integrity concerns, and the need for human oversight when AI supports security decision-making. 3. Thwarting AI-Enabled Cyber Attacks (Thwart) Adversaries are increasingly using AI to scale phishing, evade detection, and automate attacks. This focus area addresses how organizations must anticipate and counter AI-enabled threats by building resilience, improving detection of AI-driven attack patterns, and preparing for a rapidly evolving threat landscape where AI is weaponized. Why This Matters Together, Secure, Defend, and Thwart provide a practical structure for aligning AI initiatives with existing cybersecurity programs. By mapping AI-specific considerations to CSF 2.0 outcomes (Govern, Identify, Protect, Detect, Respond, Recover), the Cyber AI Profile helps organizations integrate AI security into familiar risk management practices. This is a preliminary draft, and NIST is seeking public feedback through January 30, 2026. If your organization is building, deploying, or defending with AI, now is the time to review and contribute. 🔗 https://lnkd.in/e-ETZXH8

The Unseen Threat: Is AI Making Our Cybersecurity Weaknesses Easier to Exploit? AI in cybersecurity is a double-edged sword. On one hand, it strengthens defenses. On the other, it could unintentionally expose vulnerabilities. Let’s break it down. The Good: - Real-time Threat Detection: AI identifies anomalies faster than human analysts. - Automated Response: Reduces time between detection and mitigation. - Behavioral Analytics: AI monitors network traffic and user behavior to spot unusual activities. The Bad: But, AI isn't just a tool for defenders. Cybercriminals are exploiting it, too: - Optimizing Attacks: Automated penetration testing makes it easier for attackers to find weaknesses. - Automated Malware Creation: AI can generate new malware variants that evade traditional defenses. - Impersonation & Phishing: AI mimics human communication, making scams more convincing. Specific Vulnerabilities AI Creates: 👉 Adversarial Attacks: Attackers manipulate data to deceive AI models. 👉 Data Poisoning: Malicious data injected into training sets compromises AI's reliability. 👉 Inference Attacks: Generative AI tools can unintentionally leak sensitive info. The Takeaway: AI is revolutionizing cybersecurity but also creating new entry points for attackers. It's vital to stay ahead with: 👉 Governance: Control over AI training data. 👉 Monitoring: Regular checks for adversarial manipulation. 👉 Security Protocols: Advanced detection for AI-driven threats. In this evolving landscape, vigilance is key. Are we doing enough to safeguard our systems?

The Cybersecurity and Infrastructure Security Agency (CISA), together with other organizations, published "Principles for the Secure Integration of Artificial Intelligence in Operational Technology (OT)," providing a comprehensive framework for critical infrastructure operators evaluating or deploying AI within industrial environments. This guidance outlines four key principles to leverage the benefits of AI in OT systems while reducing risk: 1. Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle.  2. Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration 3. Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance.  4. Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans. The guidance recommends addressing AI-related risks in OT environments by: • Conducting a rigorous pre-deployment assessment. • Applying AI-aware threat modeling that includes adversarial attacks, model manipulation, data poisoning, and exploitation of AI-enabled features. • Strengthening data governance by protecting training and operational data, controlling access, validating data quality, and preventing exposure of sensitive engineering information. • Testing AI systems in non-production environments using hardware-in-the-loop setups, realistic scenarios, and safety-critical edge cases before deployment. • Implementing continuous monitoring of AI performance, outputs, anomalies, and model drift, with the ability to trace decisions and audit system behavior. • Maintaining human oversight through defined operator roles, escalation paths, and controls to verify AI outputs and override automated actions when needed. • Establishing safe-failure and fallback mechanisms that allow systems to revert to manual control or conventional automation during errors, abnormal behavior, or cyber incidents. • Integrating AI into existing cybersecurity and functional safety processes, ensuring alignment with risk assessments, change management, and incident response procedures. • Requiring vendor transparency on embedded AI components, data usage, model behavior, update cycles, cybersecurity protections, and conditions for disabling AI capabilities. • Implementing lifecycle management practices such as periodic risk reviews, model re-evaluation, patching, retraining, and re-testing as systems evolve or operating environments change.

Most companies still follow the old cybersecurity playbook: 1. Buy antivirus 2. Trust the default firewall 3. Hope a data breach never happens 4. React chaotically when it does 5. Spend even more after damage is done The new, AI-driven cybersecurity approach flips this: 1. Proactively identify threats 2. Use AI for threat intelligence and gap analysis 3. Implement zero-trust architecture 4. Automate detection and response 5. Continuously refine with real-time data The hard truth? Most data breaches (and the resulting financial devastation) happen because organizations rely on outdated, reactive measures. But that was before AI. I’ve spent years mitigating breaches that could have been prevented with proactive measures. Now, with the right AI-driven framework, you can avert catastrophic threats in days, not months. Here’s my 5-step AI-enabled cybersecurity framework to save your company from hefty fines, lost trust, and public embarrassment: 1. Asset Discovery & Prioritization • Use AI-powered scanners (like Censys or Shodan) to find every exposed asset you have. • Feed the list into ChatGPT or other AI tools to categorize them by risk level. • If you don’t know what you’re defending, you’ve already lost. 2. Threat Intelligence & Gap Analysis • Tap into threat intel feeds (MITRE ATT&CK, VirusTotal, open-source repos). • Ask AI to compare your network or app vulnerabilities against known exploits. • No deep intel on emerging threats? That’s a glaring gap. 3. Automated Penetration Testing • Old approach: hire pen testers once or twice a year. • New approach: continuous AI-driven pentests that probe your environment 24/7. • If the AI tool cracks through your defenses easily, it’s time to upgrade your armor. 4. Zero-Trust Implementation • Grant “least privileged” access—no one gets more than they absolutely need. • Use AI to monitor user behaviors for anomalies (e.g., logging in from new locations, odd times). • Trust but verify. Actually, don’t trust—verify everything. 5. Incident Response Optimization • Replace static incident playbooks with AI-updated procedures. • Use machine learning to accelerate root cause analysis. • Automate common remediation steps. • If your IR plan is collecting dust in a binder, you’re already behind the curve. This isn’t just a few security patches—it’s a transformative shift. AI makes cybersecurity continuous, adaptive, and deeply data-driven. The result? • Fewer vulnerabilities slipping through the cracks • Faster response times for any incidents that do occur • Significantly reduced risk of financial and reputational damage You can keep plugging holes after breaches happen—or harness AI to build a virtually watertight security posture before it’s too late. … It’s your move. …

AI is no longer just a productivity booster — it’s a security risk multiplier. Yet most organizations are still assessing AI like traditional IT — and that’s a costly mistake. An AI Security Risk Assessment must go beyond infrastructure and focus on: ✅ Model Hallucination & Manipulation (prompt injection, jailbreaks) ✅ Sensitive Data Leakage (accidental training, unlogged API calls) ✅ Shadow AI & Unapproved Integrations ✅ Compliance Risks — GDPR, DPDP, ISO 42001, NIST AI RMF ✅ AI Supply Chain & Third-Party Model Trustworthiness ✅ Continuous Monitoring — not one-time assessment Companies that treat AI risk as a checkbox exercise today… will face a crisis tomorrow. AI is a strategic advantage — only if governed like a critical asset, not a cool tool. Are you already integrating AI risk into your enterprise GRC strategy? --- #AI #AIsecurity #AIGovernance #AIRiskAssessment #CyberSecurity #ISO42001 #NIST #GenAI #DataProtection #AICompliance #GRC #CISO #RiskManagement

CERT-In’s New AI Vulnerability Guidelines: A Wake-Up Call for OEMs and Technology Providers Artificial Intelligence is no longer just transforming business. It is transforming cyber warfare. With the release of its “Guidelines regarding AI-Accelerated Vulnerability Protection and Response Requirements for OEMs and Technology Providers” (10 June 2026), the Indian Computer Emergency Response Team (CERT-In) has signalled a fundamental shift in cybersecurity expectations. For the first time, regulators are explicitly recognising that AI is not only a defensive tool but also an offensive weapon capable of accelerating vulnerability discovery, exploit generation, credential compromise, and #cyberattack s at machine speed. KEY TAKEAWAYS : -AI-assisted vulnerability testing is now expected as part of cybersecurity assessments. -OEMs are expected to maintain Software Bills of Materials (SBOMs), dependency inventories, cryptographic inventories, and even AI component inventories. -Critical and High-Severity vulnerabilities must be disclosed immediately to affected organisations and CERT-In, along with mitigation guidance. -Zero-day vulnerabilities and AI-assisted exploitation incidents require immediate notification and coordinated response mechanisms. -Accelerated patch timelines have been prescribed, reflecting the reality that AI can weaponise vulnerabilities faster than traditional security teams can respond. -Secure Development Lifecycle (SDL), AI-assisted code review, dependency analysis, penetration testing, and supply chain security controls are becoming baseline expectations rather than best practices. My Perspective This document is more than a cybersecurity guideline. It is India’s first serious regulatory acknowledgement that the cyber threat landscape has entered the era of AI-speed attacks. The message from CERT-In is clear: If attackers are using AI to discover vulnerabilities in hours, organisations cannot continue managing vulnerabilities in weeks. Boards, #CISO s, CTOs, cloud providers, software vendors, telecom operators, fintech companies, and critical infrastructure operators should carefully review these guidelines. #Cyber resilience will increasingly be measured not by whether an organisation is breached, but by how rapidly it can detect, disclose, patch, and recover. In the age of AI, speed itself becomes a security control. Question: Will #AI become the greatest cybersecurity defender, or the most powerful cyber weapon ever created? #CyberSecurity #ArtificialIntelligence #CERTIn #CyberLaw #DataProtection #CyberRisk #AIGovernance #VulnerabilityManagement #CISO #DigitalTrust #publicpolicy #AIlaws #genai #GrC #dpdpa

Coalition for Cybersecurity in Asia-Pacific (CCAPAC): #AI and #Cybersecurity Artificial Intelligence (AI) is becoming a pivotal force in driving innovation across various sectors, including healthcare, finance, transportation, and manufacturing. However, as AI is increasingly integrated into critical systems and infrastructures, it introduces significant cybersecurity risks that must be effectively managed. This report provides a comprehensive analysis of these risks and offers strategic guidance for developing robust policies to mitigate them in the Asia-Pacific region. The report begins by exploring the current AI landscape, categorizing AI systems into predictive and generative types, and detailing their applications across key sectors. While AI holds the potential to deliver transformative advancements, it also brings unique vulnerabilities, such as data poisoning, model evasion attacks, and ethical concerns, which could compromise the integrity and security of AI systems. To address these challenges, we propose a holistic framework for AI cybersecurity. This framework includes key components such as oversight, lifecycle management, model security, data governance, transparency, and incident response strategies. It is designed to integrate seamlessly with existing laws and internationally recognized standards, ensuring a cohesive and effective approach to AI governance across the Asia-Pacific region. This policy recommendations emphasize the need to update national cybersecurity strategies, establish AI-specific guidelines, invest in research and development, foster international cooperation, and promote AI literacy. These measures are crucial to ensure that the deployment of AI systems is both secure and ethical, supporting innovation while proactively addressing emerging threats. The importance of a coordinated effort among governments, industries, and academia to develop and implement robust AI cybersecurity policies cannot be understated. Such policies are essential not only for protecting against current risks but also for anticipating and mitigating future challenges, ensuring that AI continues to be a driving force for positive change in the region.

Dear AI and Cybersecurity Auditors, AI changes how risk enters your environment and expands your attack surface. Traditional cybersecurity controls no longer cover model behavior, training data, prompts, agents, and AI-driven decisions. This draft extends NIST CSF 2.0 into AI systems. It treats models, data, prompts, agents, and AI decisions as real cyber assets. It also addresses how attackers already use AI to scale speed, deception, and impact. Here is why this framework matters for security, risk, and audit leaders. 📌 AI expands the attack surface beyond infrastructure into training data, models, prompts, agents, and third-party AI services 📌 Governance shifts from IT ownership to enterprise accountability with clear risk ownership, oversight, and decision authority 📌 Traditional controls still apply, but AI requires added focus on model integrity, data provenance, output reliability, and human oversight 📌 The framework maps AI risk directly to CSF functions so teams avoid parallel AI security programs 📌 Defensive teams use AI to reduce alert fatigue, improve detection accuracy, and support faster incident response 📌 Adversaries already use AI for phishing, malware generation, social engineering, and automated attack orchestration 📌 Continuous monitoring extends beyond systems into model drift, hallucinations, and unexpected behavior 📌 Risk tolerance must account for AI failure modes, not only system outages or data loss 📌 Audit and assurance teams gain a structured way to test AI controls across Secure, Defend, and Thwart focus areas 📌 The profile supports assessment, control design, and executive reporting without adding unnecessary complexity AI security fails when teams treat AI as software. NIST IR 8596 reframes AI as a risk domain inside cybersecurity. If your organization builds, buys, or relies on AI, this profile gives you a practical path to govern, secure, and defend it with intent. #NIST #Cybersecurity #AIGovernance #AIRisk #AIControls #ITAudit #CyberRisk #AISecurity #GRC #CSF #CyberVerge ♻��� Share this with your team or repost so more professionals. 👉Follow Nathaniel Alagbe for more.

🔒 AI Governance Meets Cybersecurity: Strengthening Data Protection with ISO Standards🔒 With every new advancement, the intersection of #AIGovernance and #cybersecurity is becoming more critical. Both disciplines aim to safeguard data, mitigate risks, and ensure ethical, secure, and compliant systems. By integrating frameworks like #ISO42001 (AI governance) with #ISO27001 (information security) and #ISO27701 (privacy management), your business can address both AI-specific and broader information security threats. ➡️ The Cyber-AI Connection AI governance and cybersecurity share key objectives: 🔸Risk Management – AI introduces unique risks like model manipulation or bias, complementing traditional cybersecurity concerns like data breaches. 🔸Data Protection – AI systems rely on secure and accurate data for training and operations. 🔸Trust and Accountability – Both focus on building systems that are auditable and trustworthy. Integrating AI governance with cybersecurity frameworks strengthens your ability to manage risks across all of these domains. ➡️ Aligning ISO42001 with ISO27001/27701 1️⃣ Information Security in AI Systems 🔸 ISO27001 addresses secure information handling. ISO42001 expands this to include safeguards for training data, models, and outputs. 📝 Example: Protecting customer data during AI model training helps ensure it’s secure at all stages. 2️⃣ Privacy Protection 🔸 ISO27701 ensures compliance with privacy regulations like GDPR. AI governance aligns this by managing personal data in training and predictions. 📝 Example: Anonymizing data used in AI models ensures compliance and ethical use. 3️⃣ Incident Response and Monitoring 🔸 ISO27001 requires incident response. ISO42001 adds focus on AI-specific issues like data poisoning or model drift, with a focus on communication of incidents to appropriate stakeholders. ➡️ Leveraging MITRE ATLAS and OWASP Top 10 for AI 1️⃣ MITRE ATLAS This knowledge base outlines adversarial techniques targeting AI, helping organizations identify threats like model evasion or inference attacks. 📝 Example: A healthcare AI model can use ATLAS to mitigate adversarial perturbations that compromise diagnostics. 2️⃣ OWASP Top 10 for AI This framework identifies common AI vulnerabilities like insecure data pipelines and inadequate robustness. Addressing these aligns with ISO27001/27701 controls. 📝 Example: Ensuring robust logging for AI fraud detection systems supports traceability and security. ➡️ Practical Steps for Integration ◻️Expand the scope of ISO27001/27701 certifications to include AI risks. ◻️Use MITRE ATLAS and OWASP to identify AI vulnerabilities and mitigate them within ISO controls. ◻️Strengthen incident response to include AI-specific threats. ◻️Train teams to understand the overlap of AI governance and cybersecurity. 🌐 References: https://genai.owasp.org/ https://atlas.mitre.org/ A-LIGN #TheBusinessofCompliance #ComplianceAlignedtoYou