How Cybersecurity Teams can Combat AI Threats

The Cybersecurity and Infrastructure Security Agency together with the National Security Agency, the Federal Bureau of Investigation (FBI), the National Cyber Security Centre, and other international organizations, published this advisory providing recommendations for organizations in how to protect the integrity, confidentiality, and availability of the data used to train and operate #artificialintelligence. The advisory focuses on three main risk areas: 1. Data #supplychain threats: Including compromised third-party data, poisoning of datasets, and lack of provenance verification. 2. Maliciously modified data: Covering adversarial #machinelearning, statistical bias, metadata manipulation, and unauthorized duplication. 3. Data drift: The gradual degradation of model performance due to changes in real-world data inputs over time. The best practices recommended include: - Tracking data provenance and applying cryptographic controls such as digital signatures and secure hashes. - Encrypting data at rest, in transit, and during processing—especially sensitive or mission-critical information. - Implementing strict access controls and classification protocols based on data sensitivity. - Applying privacy-preserving techniques such as data masking, differential #privacy, and federated learning. - Regularly auditing datasets and metadata, conducting anomaly detection, and mitigating statistical bias. - Securely deleting obsolete data and continuously assessing #datasecurity risks. This is a helpful roadmap for any organization deploying #AI, especially those working with limited internal resources or relying on third-party data.

The 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 guidance from 𝗗𝗛𝗦/𝗡𝗦𝗔/𝗙𝗕𝗜 outlines best practices for securing data used in AI systems. Federal CISOs should focus on implementing a comprehensive data security framework that aligns with these recommendations. Below are the suggested steps to take, along with a schedule for implementation. 𝗠𝗮𝗷𝗼𝗿 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Establish Governance Framework     - Define AI security policies based on DHS/CISA guidance.     - Assign roles for AI data governance and conduct risk assessments.  2. Enhance Data Integrity     - Track data provenance using cryptographically signed logs.     - Verify AI training and operational data sources.     - Implement quantum-resistant digital signatures for authentication.  3. Secure Storage & Transmission     - Apply AES-256 encryption for data security.     - Ensure compliance with NIST FIPS 140-3 standards.     - Implement Zero Trust architecture for access control.  4. Mitigate Data Poisoning Risks     - Require certification from data providers and audit datasets.     - Deploy anomaly detection to identify adversarial threats.  5. Monitor Data Drift & Security Validation     - Establish automated monitoring systems.     - Conduct ongoing AI risk assessments.     - Implement retraining processes to counter data drift.  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  Phase 1 (Month 1-3): Governance & Risk Assessment   • Define policies, assign roles, and initiate compliance tracking.   Phase 2 (Month 4-6): Secure Infrastructure   • Deploy encryption and access controls.   • Conduct security audits on AI models. Phase 3 (Month 7-9): Active Threat Monitoring • Implement continuous monitoring for AI data integrity.   • Set up automated alerts for security breaches.   Phase 4 (Month 10-12): Ongoing Assessment & Compliance   • Conduct quarterly audits and risk assessments.   • Validate security effectiveness using industry frameworks.  𝗞𝗲𝘆 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀   • Collaboration: Align with Federal AI security teams.   • Training: Conduct AI cybersecurity education.   • Incident Response: Develop breach handling protocols.   • Regulatory Compliance: Adapt security measures to evolving policies.  

🚨 Generative AI is fueling the dark web, and it's opening a new frontier for cybercrime. This isn’t a distant threat—it’s happening now. Cybercriminals are using generative AI models to create more sophisticated malware and ransomware, making traditional defenses look outdated. Here’s the reality: Generative AI can: 👉 Auto-generate malicious code like ransomware and malware 👉 Develop attacks that evolve faster than security patches can keep up 👉 Produce phishing campaigns that look more convincing than ever before But you can fight back. 🛡️ Here’s how to stay ahead of AI-fueled cybercrime: 1️⃣ Adopt AI-Powered Security Solutions → Just as criminals use AI to create malware, you should leverage AI tools that can predict and respond to these emerging threats. 2️⃣ Harden Your Defenses Against AI-Generated Code → Generative AI can craft malware that constantly evolves. → Use advanced threat detection systems that can monitor patterns of behavior, not just signatures. 3️⃣ Strengthen Ransomware Response Plans → Ransomware is becoming easier for criminals to create. → Regularly update your backups and practice ransomware recovery drills. 4️⃣ Boost Employee Training on Phishing → AI can produce highly convincing phishing emails. → Invest in continuous training to ensure employees can spot even the most deceptive phishing attempts. 5️⃣ Monitor Dark Web Activity → Stay informed on the latest AI-driven threats by actively tracking dark web chatter. → Threat intelligence can provide early warning signs before attacks hit your system. AI is revolutionizing cybercrime, but with the right tools and mindset, you can stay ahead of these threats. 👉 Are you ready to protect your organization from the new wave of AI-generated attacks? Let’s talk about how to secure your future.

AI is the New Insider Threat – And It’s Already Inside the Building Once upon a time, insider threats were disgruntled employees, careless users, or rogue contractors. Now? They don’t even need to exist. AI-powered identity theft is changing the game. Attackers are no longer just phishing employees—they’re impersonating them, deepfaking voices, cloning credentials, and bypassing security with terrifying accuracy. It’s no longer about who you trust, but what you trust. And while businesses scramble to integrate AI into decision-making, attackers are using it to automate fraud, bypass security, and exploit human and machine identities. The result? An identity landscape more vulnerable than ever. Three Trends That Should Terrify Every CISO Right Now: 🔹 Deepfake Impersonation Attacks Are Getting Smarter – AI-generated voices, emails, and even video calls make it nearly impossible to distinguish real employees from fake ones. (Your boss just called? Are you sure it was them?) 🔹 Machines Are the New Humans – AI bots, service accounts, and machine identities now outnumber human users in many organizations. Attackers know this—and they’re stealing, abusing, and compromising them faster than security teams can respond (so give them more grace). 🔹 Zero Trust is No Longer Optional – Traditional security models assumed trust based on credentials. That’s not enough anymore. Every request, every user (human or machine), every access point must be verified. How to Fight Back Against AI-Powered Identity Theft: ✅ Adopt Continuous Behavioral Monitoring – If identity can be faked, behavior is harder to spoof. Look for anomalies in user and machine actions. ✅ Reinforce Authentication Beyond MFA – Hardware tokens, biometric verification, and AI-driven risk analysis are must-haves. ✅ Secure Machine Identities – Don’t just protect human logins—monitor API keys, bots, and service accounts with the same level of scrutiny. ✅ Train Employees to Spot AI-Powered Attacks – Teach teams how deepfake social engineering works—because if it looks and sounds real, they’ll fall for it. We’re entering a world where "trust, but verify" is no longer enough. It’s verify everything, trust nothing. #AIThreats #InsiderThreat #CyberSecurity #ZeroTrust #HumanRisk