How AI Transforms Security Practices

AI is changing the game in cybersecurity, and not just for defenders. Adversaries are using it too, scaling attacks, compressing the time from vulnerability discovery to exploitation and increasing the volume of threats enterprises face every single day. The ground is moving fast and organizations that treat security as an afterthought will feel it. Here are a few things I'm thinking about: • Run what's current. Retire what's not. Legacy systems with outdated software are one of the most reliable attack vectors out there. Reducing technical debt is a security imperative. If you're multiple versions behind, you're already behind the threat. Treat end-of-life timelines like a countdown clock, not a suggestion. • You can't fix what you can't see. A comprehensive, continuously updated inventory of every hardware, software, and cloud asset is foundational. Enrich those records with ownership, criticality and exposure data so that when a new threat emerges, you can answer "where are we exposed?" in minutes, not days. • Practice resilience before you need it. Tabletop exercises and live simulations aren't optional. Plans that haven't been tested under realistic conditions will fail under real pressure. Include senior leadership, legal and communications teams so that decision-making under pressure is rehearsed, not improvised. • Embed security into AI from day one. AI is simultaneously a threat accelerant and a powerful defensive capability. Treat AI models, training data and inference pipelines as high-value assets. Validate AI-generated code with the same rigor as human-authored code. And recognize that adversaries are using AI to automate exploitation. Invest in AI-augmented defenses to match the pace of the threat. The resilience to operate through this technological change requires urgency, discipline, and rigorous execution of the fundamentals. Security isn't a feature we add at the end—it's the foundation we build from the start. That's how we protect our customers, our firm, and the trust that makes everything else possible. What security practices are you doubling down on this year? Drop your thoughts below.

Top AI Agent Use Cases Transforming Cybersecurity Most people think cybersecurity is about reacting to attacks. Until they realize they’re already compromised. It’s not always ransomware or loud breach alerts. Sometimes it’s subtle, almost invisible—but just as dangerous. ⚠️ The SIEM logs no one has time to monitor. ⚠️ The endpoint behaving slightly off, but ignored. ⚠️ The phishing email that slips past traditional filters. Here’s how AI agents are changing the game and protecting organizations before attacks even happen: Threat Detection & Triage • Process massive SIEM telemetry at lightning speed • Correlate logs humans would never catch • Generate actionable alerts for your team Automated Incident Response • Trigger playbooks instantly to contain threats • Revoke tokens, isolate endpoints, or block access • Recover faster with minimal human intervention Anomaly & Behavior Analysis • Spot subtle shifts in user or application behavior • Detect patterns beyond static rules • Reduce insider threat risks and breaches Zero-Day Identification • Analyze codebases and dependencies before CVEs exist • Predict vulnerabilities with AI modeling • Receive risk reports before attackers exploit flaws AI Code Scanning • Go beyond syntax checks to detect logic flaws • Generate remediation code automatically • Reduce security debt in development pipelines Phishing Defense • Analyze email behavior and access patterns • Identify advanced phishing or account takeover attempts • Take mitigation actions before damage occurs Your next steps matter: → Implement AI-driven monitoring today → Automate repetitive response tasks → Train your team on anomaly detection Remember: cybersecurity isn’t reactive anymore. It’s proactive, predictive, and automated. And if your organization still waits for alerts? Your data, your clients, and your reputation are at risk. If this resonates, repost for your network. Follow Marcel Velica for more AI + Cybersecurity insights.

Not long ago, attackers needed a team, weeks of planning, and a lot of trial and error to breach a system. Today, a well-tuned AI model can orchestrate an attack end-to-end without a human hand to guide it. The fact that AI can advance on its own and operate much faster than a human makes protecting sensitive information and systems a more difficult problem. Difficult doesn’t mean impossible. At Equifax, we’ve already seen AI make a difference: • Automated and AI-driven detection slashing our mean-time-to-detect to under 60 seconds. • Automated anomaly hunting, lighting up blind spots for us in real time before they become breaches. • Red teams using LLMs to safely simulate adversaries and close gaps faster. Threat actors aren’t waiting to upskill on AI and neither should security teams. Here are 3 actions I recommend: • Build AI literacy across all security roles, not just data scientists. • Treat AI-powered adversaries as your baseline threat model, not a future risk. • Lean into partnerships. The AI security community is your force multiplier. As AI continues its rapid advancement, it's inevitable that both technology and attackers will evolve. Our focus must be on ensuring security teams outpace these evolving threats. 🛡️ #AI #Cybersecurity #Innovation #LLM #SecurityCommunity

AI is increasingly moving into the control plane of our digital platforms, and that shift has profound implications for cybersecurity. Much of today’s AI discussion focuses on productivity and automation. Important topics, but not the most consequential from a security perspective. What matters more is where AI is being embedded. Increasingly, it is becoming part of the control layers we depend on, including identity, access, analytics, decision support, and security tooling itself. Cybersecurity has traditionally focused on protecting data: where it resides, who can access it, and how it is encrypted. These concerns remain essential, but they are no longer sufficient. AI systems do more than process information. They infer, prioritise, adapt, and influence behaviour. As AI becomes embedded in security-relevant platforms, the core question shifts from where data is stored to who controls system behaviour. From a security perspective, control equals trust. As AI capabilities advance, some long-standing assumptions about static trust need to be re-examined. Systems are updated frequently, operate across platforms and jurisdictions, and increasingly act autonomously. In this environment, trust cannot be implicit. It must be continuously established, verified, and monitored. Protecting customer data therefore means protecting the whole system. Data flows through identities, platforms, APIs, and AI-driven components. When AI influences these flows, security requires transparency, accountability for automated decisions, the ability to intervene, and resilience when dependencies change or fail. At SEB, we approach AI with both ambition and discipline. Our focus is on strong control, continuous verification, and resilience by design. AI does not reduce our responsibility for cybersecurity. It increases it. The real question is not whether AI will change cybersecurity. It already has. The question is whether we are prepared for what that change truly means.

"Artificial intelligence (AI) is rapidly reshaping the cyber security landscape. As highly capable AI becomes more widely available, malicious actors are using it to deliver cyber threats at greater scale and speed. Organisations that don’t re-evaluate and improve their defences will remain vulnerable to these AI-enabled cyber threats. Cyber security has traditionally relied on specialised teams and reactive workflows to manage risk. These approaches remain important, but the scale and complexity of the modern cyber security landscape increasingly strain them. Heavy dependence on manual processes can make it difficult to prioritise risks, investigate potential threats and maintain consistent defensive coverage. AI presents a significant opportunity for cyber defenders. When used safely, securely and responsibly AI can: • strengthen prioritisation of cyber risks • improve detection of threats and vulnerabilities • support faster response and recovery • reduce reliance on repetitive manual tasks. This guidance outlines how organisations can use AI to strengthen organisational cyber security while managing the risks of using AI. It outlines how the cyber security landscape is evolving and describes how organisations can use AI aligned with the Information security manual (ISM) cyber security functions of Govern, Identify, Protect, Detect, Respond and Recover. It also sets out principles for securely adopting AI, along with key questions for cyber defenders to ask AI vendors to support secure use. Human oversight, governance and Secure by Design practices remain essential. AI can significantly enhance cyber security, but it is not a replacement for strong cyber security fundamentals. Poorly designed or poorly governed AI systems can introduce new attack paths. This can occur through excessive system access, reliance on untrusted inputs, or automated actions without adequate safeguards." Australian Signals Directorate 

𝐌𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐫𝐞 𝐫𝐮𝐬𝐡𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲 𝐀𝐈 𝐢𝐧𝐭𝐨 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. Very few are building the security architecture required to operate it safely at scale. That is becoming one of the biggest enterprise risks in 2026. Because AI systems are no longer isolated applications. They are increasingly connected to enterprise data, APIs, workflows, decision systems, and autonomous agents. Which means AI security is evolving into an entirely new operational discipline. The strongest organisations now understand: AI security is not a single control layer. It is a full-stack governance architecture. 𝐓𝐡𝐚𝐭 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬: → Identity-aware AI access control → Sensitive data protection layers → Prompt and input threat filtering → Model integrity and version governance → Output validation and policy enforcement → Continuous AI observability and monitoring Because once AI systems begin influencing business operations… Security failures no longer remain technical incidents. They become operational, financial, and reputational risks. The companies building durable AI advantage are not simply deploying more intelligent systems. They are building environments where intelligence operates within trusted, observable, and governed boundaries. That is what production-grade AI maturity looks like. Because in enterprise AI… Trust is infrastructure. P.S. Many organisations still approach AI security using traditional application security thinking. The more mature organisations are redesigning security architectures specifically for autonomous and AI-driven systems. Follow Ashish Sahu for more insights

𝐌𝐨𝐬𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐚𝐫𝐞 𝐬𝐭𝐢𝐥𝐥 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐬 𝐚 𝐟𝐮𝐭𝐮𝐫𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. It is already a present-day attack surface. In 2026, AI is no longer just a productivity layer. It is a security boundary. And attackers have already adapted. 𝐖𝐞 𝐚𝐫𝐞 𝐧𝐨𝐰 𝐬𝐞𝐞𝐢𝐧𝐠 𝐚 𝐜𝐥𝐞𝐚𝐫 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐨𝐟 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐭𝐡𝐫𝐞𝐚𝐭𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬: → Prompt injection attacks bypassing safeguards → Model inversion exposing sensitive training data → Supply chain poisoning of AI models → Data leakage through embeddings and vector databases → Agent jailbreaks bypassing safety controls → Third-party tool compromise via AI integrations → Context window manipulation hiding malicious intent The attack surface is no longer static. It is dynamic, probabilistic, and deeply embedded inside AI workflows. This fundamentally changes how security leaders must respond. Traditional perimeter thinking no longer applies. 𝐃𝐞𝐟𝐞𝐧𝐬𝐞 𝐦𝐮𝐬𝐭 𝐧𝐨𝐰 𝐬𝐡𝐢𝐟𝐭 𝐭𝐨𝐰𝐚𝐫𝐝: → Input layer protection for prompt integrity → Model layer governance and testing → Output validation and hallucination control → Infrastructure-level AI monitoring and controls 𝐀𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐭𝐢𝐦𝐞, 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐦𝐮𝐬𝐭 𝐩𝐫𝐞𝐩𝐚𝐫𝐞 𝐟𝐨𝐫 𝐭𝐡𝐫𝐞𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐢𝐞𝐬: → Attackers will target AI agents as entry points → Data governance failures will become primary breach vectors → AI systems will increasingly influence business decisions directly This is not just a technical evolution. It is a leadership challenge. Because securing AI systems is no longer about controlling tools. It is about controlling trust, data flow, and decision intelligence at scale. Organizations that treat AI security as a side concern will fall behind quickly. Those that integrate security into every AI layer will define the next era of resilient enterprises. Cyber Leadership Academy Follow Vijay Banda for more insights

🚀 AI Is Transforming Cybersecurity in 2026 — And We’re Just Getting Started This year is shaping up to be one of the most dynamic periods of change we’ve seen across the cybersecurity landscape. AI is no longer a distant enabler — it’s becoming woven into the core of our cyber tech stack, fundamentally reshaping how we defend, detect, and decide. Here are three areas that I am most excited about: AI‑Driven Decisions for Access Management The shift toward continuous, adaptive access is accelerating. AI-powered identity models can now evaluate real-time context, user behavior, and risk signals to make smarter, faster access decisions. This is helping organizations significantly reduce over‑permissioning while improving user experience — a balance we’ve been chasing for years. Smarter Incident Response & Fewer False Positives AI-driven detection and response systems are maturing fast. We’re seeing tools that not only correlate signals more effectively but also explain their reasoning with greater clarity, enabling analysts to trust and act with confidence. The reduction in false positives is creating more space for teams to focus on what matters: hunting, improving controls, and getting ahead of attackers. A New Era for Insider Threat Models Insider risk programs are being reimagined with AI that understands patterns — not just events. Instead of reacting to alerts, teams can now leverage behavioral baselines, anomaly detection, and predictive insights to identify risk earlier and intervene more constructively. It’s an evolution toward more proactive, more human‑centric insider threat management. As AI continues to integrate across the entire cyber ecosystem, one thing is clear - 2026 will be a defining year in how organizations operationalize intelligence at scale. What AI-driven transformations are you most excited about this year?

As AI reshapes the threat landscape, the AI Cybersecurity Dimensions (AICD) Framework helps tackle the complexities of AI-driven cyber threats. The AICD Framework breaks down threats into three critical dimensions: 1) Defensive AI: Using AI to enhance security systems, from intrusion detection to anomaly detection. 2) Offensive AI: Understanding how attackers leverage AI to automate and amplify attacks like deepfake phishing, adaptive malware, and advanced social engineering. 3) Adversarial AI: Targeting vulnerabilities within AI models themselves—such as data poisoning—that can mislead or manipulate AI systems. The framework offers three concrete steps for strengthening defenses against AI-driven attacks: 1️⃣ Upgrade Detection with Adaptive AI: Move beyond static detection methods. Implement AI-based monitoring that continuously learns from new attack patterns. Schedule regular model updates so detection capabilities stay one step ahead of evolving AI-driven threats like deepfake phishing and adaptive malware. Admittedly, this is easier said than done at this stage of the AI game. 2️⃣ Fortify AI Models Against Adversarial Attacks: Secure your AI by testing models for vulnerabilities like data poisoning and evasion attacks. Use adversarial training, which includes feeding manipulated inputs during model development, to make your AI robust against tampering and deceptive inputs. 3️⃣ Establish Sector-Wide Standards and Training: Develop and enforce cross-sector standards specific to AI security practices. Partner with industry and policy groups (like the Cloud Security Alliance and NIST) to create consistent guidelines that address AI vulnerabilities. Hold quarterly training sessions on AI-specific threats to keep your team’s skills sharp and up-to-date. By focusing on these steps, organizations can put the AICD Framework to work in meaningful, practical ways. How is your team adapting to the rise of AI-driven cyber threats? Caleb Sima Cloud Security Alliance American Society for AI #CyberSecurity #AI #CyberDefense

How I Use AI as a Cybersecurity Leader: 5 Key Wins That Elevate My Leadership As a cybersecurity leader, staying ahead of the curve is not just a goal—it’s a necessity. Artificial Intelligence (AI) has become one of the most powerful enablers in my professional toolkit. Here are five key ways AI enhances my leadership and enables me to drive success in my role. 1. Proactive Threat Detection Cyber threats are becoming more sophisticated, and traditional approaches to detection often lag behind. AI-powered tools help me identify anomalies and potential breaches before they escalate into full-blown incidents. These tools analyse vast amounts of data in real time, flagging unusual patterns that might be invisible to the human eye. Leadership Win: By anticipating risks, I can focus my team’s efforts on strategic initiatives rather than firefighting, fostering a proactive security culture. 2. Accelerated Incident Response Time is critical during a cyber incident. AI tools streamline the incident response process by automating repetitive tasks such as log analysis, prioritizing alerts, and even suggesting remediation steps. Leadership Win: With AI managing the routine, I empower my team to focus on high-impact decisions, enhancing their efficiency and job satisfaction. 3. Enhanced Decision-Making Through Data Insights AI doesn’t just provide raw data—it transforms it into actionable insights. By analyzing trends, identifying vulnerabilities, and even predicting potential attack vectors, AI equips me with the information needed to make informed decisions. Leadership Win: Data-backed decisions build trust with executives and ensure our security strategies align with broader business objectives. 4. Tailored Employee Training and Awareness Human error remains one of the top causes of security breaches. AI helps me identify patterns of risky behavior within the organization and tailor training programs to address these gaps. Leadership Win: By delivering targeted training, I cultivate a security-conscious culture and reduce the likelihood of human error undermining our defenses. 5. Strategic Resource Allocation In cybersecurity, resources are finite. AI allows me to allocate these resources more strategically by providing insights into where the greatest risks lie. Leadership Win: Optimizing resource use demonstrates fiscal responsibility and ensures the security team delivers maximum value to the organization. Conclusion By integrating AI into my leadership approach, I’ve been able to lead more effectively, empower my team, and align cybersecurity strategies with organizational goals. For fellow leaders considering adopting AI: Start small, learn the tools, and focus on areas where automation can deliver the most immediate impact. In an era where threats evolve daily, leveraging AI is no longer optional; it’s essential. How are you using AI in your field? Let’s discuss in the comments! #cybersecurity #infosec #AI #leadership