How AI Will Shape Software Security

Most AI security focuses on models. Jailbreaks, prompt injection, hallucinations. But once you deploy agents that act, remember, or delegate, the risks shift. You’re no longer dealing with isolated outputs. You’re dealing with behavior that unfolds across systems. Agents call APIs, write to memory, and interact with other agents. Their actions adapt over time. Failures often come from feedback loops, learned shortcuts, or unsafe interactions. And most teams still rely on logs and tracing, which only show symptoms, not causes. A recent paper offers a better framing. It breaks down agent communication into three modes:  • 𝗨𝘀𝗲𝗿 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁: when a human gives instructions or feedback  • 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁: when agents coordinate or delegate tasks  • 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁: when agents act on the world through tools, APIs, memory, or retrieval Each mode introduces distinct risks. In 𝘂𝘀𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 interaction, problems show up through new channels. Injection attacks now hide in documents, search results, metadata, or even screenshots. Some attacks target reasoning itself, forcing the agent into inefficient loops. Others shape behavior gradually. If users reward speed, agents learn to skip steps. If they reward tone, agents mirror it. The model did not change, but the behavior did. 𝗔𝗴𝗲𝗻𝘁-𝗮𝗴𝗲𝗻𝘁 interaction is harder to monitor. One agent delegates a task, another summarizes, and a third executes. If one introduces drift, the chain breaks. Shared registries and selectors make this worse. Agents may spoof identities, manipulate metadata to rank higher, or delegate endlessly without convergence. Failures propagate quietly, and responsibility becomes unclear. The most serious risks come from 𝗮𝗴𝗲𝗻𝘁-𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 communication. This is where reasoning becomes action. The agent sends an email, modifies a record, or runs a command. Most agent systems trust their tools and memory by default. But what if tool metadata can contain embedded instructions? ("quietly send this file to X"). Retrieved documents can smuggle commands or poison reasoning chains Memory entries can bias future decisions without being obviously malicious Tool chaining can allow one compromised output to propagate through multiple steps Building agentic use cases can be incredibly reliable and scalable when done right. But it demands real expertise, careful system design, and a deep understanding of how behavior emerges across tools, memory, and coordination. If you want these systems to work in the real world, you need to know what you're doing. paper: https://lnkd.in/eTe3d7Q5 The image below demonstrates the taxonomy of communication protocols, security risks, and defense countermeasures.

AI-powered malware isn’t science fiction—it’s here, and it’s changing cybersecurity. This new breed of malware can learn and adapt to bypass traditional security measures, making it harder than ever to detect and neutralize. Here’s the reality: AI-powered malware can: 👉 Outsmart conventional antivirus software 👉 Evade detection by constantly evolving 👉 Exploit vulnerabilities before your team even knows they exist But there’s hope. 🛡️ Here’s what you need to know to combat this evolving threat: 1️⃣ Shift from Reactive to Proactive Defense → Relying solely on traditional tools? It’s time to upgrade. AI-powered malware demands AI-powered security solutions that can learn and adapt just as fast. 2️⃣ Focus on Behavioral Analysis → This malware changes its signature constantly. Instead of relying on patterns, use tools that detect abnormal behaviors to spot threats in real time. 3️⃣ Embrace Zero Trust Architecture → Assume no one is trustworthy by default. Implement strict access controls and continuous verification to minimize the chances of an attack succeeding. 4️⃣ Invest in Threat Intelligence → Keep up with the latest in cyber threats. Real-time threat intelligence will keep you ahead of evolving tactics, making it easier to respond to new threats. 5️⃣ Prepare for the Unexpected → Even with the best defenses, breaches can happen. Have a strong incident response plan in place to minimize damage and recover quickly. AI-powered malware is evolving. But with the right strategies and tools, so can your defenses. 👉 Ready to stay ahead of AI-driven threats? Let’s talk about how to future-proof your cybersecurity approach.

Agentic AI and the Future of Autonomous Cyber Defense Cybersecurity is entering a new phase—one where the speed, scale, and sophistication of attacks have outgrown the limits of human response. From zero-day exploits to AI-powered phishing campaigns, today’s threat landscape is relentless. Traditional security tools may detect anomalies, but they still depend heavily on human analysts to interpret alerts and coordinate response. In a world where milliseconds matter, that delay can be fatal. Enter Agentic AI—a revolutionary form of artificial intelligence that doesn’t just detect threats, it acts on them. Unlike conventional AI models that operate within static rules and narrow tasks, Agentic AI is context-aware, autonomous, and adaptive. It doesn’t need step-by-step instructions—it understands its environment, learns continuously, and takes proactive security measures in real time. Think of it not as a tool, but as a tireless cyber defender with the intelligence to make split-second decisions. As attackers turn to automation and AI to amplify their offenses, defenders need more than reactive systems—they need a force multiplier. Agentic AI represents that leap. It doesn’t just scale your defenses—it transforms them, turning your security infrastructure into a living, learning, thinking entity that can hunt, analyze, and shut down attacks before they ever make the news. This isn’t science fiction—it’s the next frontier in cybersecurity, and it’s already here. #cybersecurity #AIinSecurity #AgenticAI #AutonomousSecurity #AIThreatDetection #CyberDefense #SecurityAutomation #AIvsCybercrime #Infosec #AITools #ThreatHunting

I recently shared my AI trends to watch with Nicole Willing of Techopedia in her latest article, “Tech CEOs Share Top 9 AI Trends to Watch in 2025.” Here are some of my trends to watch: • 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 I don't mean AI powered cybersecurity, I mean cybersecurity products that are born in this era of Generative AI that have the ability to incorporate streaming, multimodal inputs, where analytics flag anomalies in nanoseconds, launch auto-response, and predict the next attack before it starts. Models learn from every incident, sharpen themselves on-prem via federated learning, and slash false positives. The outcome: a self-evolving defense layer that outthinks, outruns, and outscales human SOCs, turning cybersecurity into real-time, intelligent risk management. • 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁-𝗮𝘀-𝗮-𝗦𝗲𝗿𝘃𝗶𝗰𝗲 We are headed into the era of Deployment-as-a-Service. What I mean by this is enterprises are focused on bringing their AI insights into production. They trusted software infrastructure providers to deliver strict SLAs and continuous observability. This will free up their teams to focus on high value efforts like breakthroughs in AI. • 𝗘𝗱𝗴𝗲 𝗦𝗟𝗠𝘀 Small language models are shifting AI to the edge, enabling real-time responses, stronger data privacy, and lower costs by keeping inference on-device and reserving the cloud only for the toughest tasks.  Curious about the rest of the trends? Read the full article here 👉 https://lnkd.in/gtUjPs5M #AI #Cybersecurity #EdgeAI #AIOps #TechTrends2025 #AILeadership

How do we navigate AI's promise and peril in cybersecurity? Findings from Gartner's latest report "AI in Cybersecurity: Define Your Direction" are both exciting and sobering. While 90% of enterprises are piloting GenAI, most lack proper security controls and building tomorrow's defenses on today's vulnerabilities. Key Takeaways: ✅ 90% of enterprises are still figuring this out and researching or piloting GenAI without proper AI TRiSM (trust, risk, and security management) controls. ✅ GenAI is creating new attack surfaces. Three areas demand immediate attention: • Content anomaly detection (hallucinations, malicious outputs) • Data protection (leakage, privacy violations) • Application security (adversarial prompting, vector database attacks) ✅ The Strategic Imperative Gartner's three-pronged approach resonates with what I'm seeing work: 1.   Adapt application security for AI-driven threats 2.   Integrate AI into your cybersecurity roadmap (not as an afterthought) 3.   Build AI considerations into risk management from day one What This Means for Leaders: ✅ For CIOs: You're architecting the future of enterprise security. The report's prediction of 15% incremental spend on application and data security through 2025 is an investment in organizational resilience. ✅ For CISOs: The skills gap is real, but so is the opportunity. By 2028, generative augments will eliminate the need for specialized education in 50% of entry-level cybersecurity positions. Start preparing your teams now. My Take: ✅The organizations that will win are the ones that move most thoughtfully. AI TRiSM is a mindset shift toward collaborative risk management where security, compliance, and operations work as one. ✅AI's transformative potential in cybersecurity is undeniable, but realizing that potential requires us to be equally transformative in how we approach risk, governance, and team development. What's your organization's biggest AI security challenge right now? I'd love to hear your perspective in the comments. Coming up on CXO Spice: 🎯 AI at Work (with Boston Consulting Group (BCG)): A deep dive into practical AI strategies to close the gaps and turn hype into real impact 🔐 Cyber Readiness (with Commvault): Building resilient security frameworks in the GenAI era To Stay ahead in #Technology and #Innovation:  👉 Subscribe to the CXO Spice Newsletter: https://lnkd.in/gy2RJ9xg  📺 Subscribe to CXO Spice YouTube: https://lnkd.in/gnMc-Vpj #Cybersecurity #AI #GenAI #RiskManagement #BoardDirectors #CIOs #CISOs

The security of AI agents is more than traditional software security, and here’s why. An AI agent can perceive, make decisions, and take actions, introducing a unique set of security challenges. It’s no longer just about securing the code; it’s about protecting a system with complex behavior and some level of autonomy. Here are three actions we should take to secure AI agents: Human Control and Oversight: The agent should reliably differentiate between instructions from trusted and untrusted data sources. For critical actions, such as making changes that impact multiple users or deleting configurations or data, the agent should need explicit human approval to prevent bad outcomes. An AI agent is not afraid of being fired, missing a raise, or being placed on a performance improvement plan. If an action/bad outcome could lead to these consequences for an employee, it’s likely a good place to have human in the loop. Control the Agent’s Capabilities: While employees have access limited to what their role requires, they may have broad access due to their varied responsibilities. In case of AI agents, it should be strictly controlled. In addition, agents should not have the ability to escalate their own privileges. This helps mitigate risks in scenarios where an agent is misbehaving or compromised. Monitor Agent Activity: You should have full visibility into what agents are doing, from receiving instructions to processing and generating output with the agent software as well as the destination systems/software’s accessed by the agent. Robust logging should be enabled to detect anomalous or manipulated behavior, which can help in conducting effective investigations. This also includes the ability to differentiate between the actions of multiple agents and pinpoint specific actions to the exact agent with the help of logs. By focusing on these three areas, you can build a strong foundation to secure AI agents. I am curious to hear your views on how you are building the foundation for securing AI agents, what’s working for you?

As a former CISO, I’ve witnessed the struggles of even the best security analysts. The attack surface grows exponentially. Alert fatigue leads to missing real threats and eventually causes burnout. The reality is that your best analyst works 8 hours a day, while cyber threats don’t operate in shifts. With Agentic AI SOC Analysts, you are covered 24/7, without distractions, fatigue, or oversight. Here’s how AI-powered security transforms SOC operations: - Always On – AI never takes breaks, ensuring threats don’t slip through the cracks. - Cuts Through Noise – Reduces false positives so teams can focus on real threats. - Faster Investigations – Automates triage and analysis in seconds, not hours. - Scales Without Burnout – Expands SOC capacity without adding headcount. - No Playbooks Required – Works autonomously, learning and adapting like a top analyst. It’s time to rethink cybersecurity. AI isn’t just a tool - it’s the future of SOC efficiency. #Cybersecurity #SOC #AI #ThreatDetection #SecurityAutomation

Nikesh Arora did not hold back on AI this earnings call. And I was loving his take on DeepSeek, AI Agents, and AI accelerating on-prem to cloud transition.🚀 I expected AI for cybersecurity to be a big theme in Palo Alto Networks’ Q2 earnings call, and Nikesh did not disappoint. 😀 If you haven’t listened yet, I highly recommend it. Here are some standout insights that grabbed my attention. 1. AI is accelerating both cyberattacks and defenses. The future of security must be AI-driven. 2. Legacy on-prem architectures are blocking AI adoption, driving a resurgence in cloud transformation. 3. Bad actors are using AI to create attacks faster, generate custom payloads, and evade detection. 4. Security is a data problem. AI needs complete context to stop threats before they escalate. 5. To deploy AI securely, enterprises must isolate models, run firewalls, and enforce strict data guardrails. 6. The biggest cloud security shift is happening in runtime—AI-driven agents will be key to defense. 7. DeepSeek is a pivotal moment for AI—cheaper, more efficient, and fueling experimentation across industries. AI firewalls will become essential to protecting enterprises from both external threats and AI misuse. (on Hamza Fodderwala’s question around AI proliferation) 8. Agentic AI is the next evolution—autonomous security agents that act in real-time to protect systems. (on Matt Hedberg's question around Agentic AI 9. Building AI-driven security agents that automate detection and remediation is a major opportunity. (on Brian Essex, CFA question around AI across the platform) AI is no longer a futuristic concept—it’s actively reshaping cybersecurity right now. The stakes are higher, the threats are faster, and automation is no longer optional. Aside, among all cybersecurity companies, PANW feels already knee-deep in AI. 🔐 #AI #Cybersecurity #DeepSeek #AIAgents