Artificial Intelligence
“AI-Generated Threat Intelligence Must be Continuously Validated to Prevent Misclassifications”

Bassel Kachfeh, Manager for Digital Solutions at Omnix, says, organizations should establish strict AI governance policies, conduct frequent audits, and maintain human oversight to validate AI-generated security decisions
How is generative AI being utilized to enhance cybersecurity measures today?
Generative AI is revolutionizing cybersecurity by automating threat intelligence, analysing attack patterns, and streamlining security operations. It enables real-time anomaly detection, AI-assisted malware reverse engineering, and phishing email analysis. Security teams leverage AI to generate proactive threat models and simulate cyberattacks for red teaming exercises. Additionally, AI-powered SOC automation enhances efficiency by summarizing security logs and facilitating rapid response. As cyber threats evolve, generative AI is playing an increasingly critical role in fortifying digital defences and reducing detection times.
What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
Despite its benefits, generative AI introduces risks such as AI-generated phishing attacks, deepfake social engineering, and automated hacking techniques. Cybercriminals exploit AI to craft convincing spear-phishing emails, generate deceptive content, and create evasive malware. AI can also be manipulated through data poisoning, leading to inaccurate threat assessments. The rise of AI-driven misinformation campaigns further complicates digital trust. Addressing these risks requires the development of AI-enhanced security controls, continuous model monitoring, and adversarial AI defences to detect and neutralize malicious AI-generated threats.
How can organizations leverage generative AI for proactive threat detection and response?
Organizations are integrating generative AI to enhance cybersecurity through real-time threat analysis, predictive modelling, and automated remediation. AI-powered playbooks streamline incident response, reducing containment times. Generative AI generates synthetic cyber threat scenarios, aiding in penetration testing and red teaming exercises. AI-driven SOC tools efficiently correlate security events, minimizing false positives and improving decision-making. By harnessing AI’s capabilities, organizations can proactively detect, assess, and neutralize emerging cyber threats before they escalate into significant security incidents.
What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
Ethical challenges in AI-driven cybersecurity include bias in threat detection models, data privacy concerns, and the potential misuse of AI-generated content. AI must be designed with transparency, explainability, and accountability to prevent unintended consequences. Organizations should establish strict AI governance policies, conduct frequent audits, and maintain human oversight to validate AI-generated security decisions. Implementing fairness-enhancing strategies, such as diverse training datasets and adversarial testing, ensures more accurate and unbiased AI threat intelligence.
What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Cybersecurity teams face obstacles such as model interpretability, high false positive rates, adversarial AI threats, and integration complexities. AI-generated threat intelligence must be continuously validated to prevent misclassifications. Additionally, integrating AI with existing security tools requires skilled personnel and robust API compatibility. Regulatory compliance adds another layer of complexity. To address these challenges, organizations should adopt AI transparency frameworks, establish human-in-the-loop validation mechanisms, and ensure continuous model training to improve AI accuracy and reliability.
Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
AI-powered cybersecurity solutions have successfully prevented cyberattacks across industries. AI-driven phishing detection tools analyze email content and flag sophisticated scams before they reach end users. Large enterprises leverage AI to conduct real-time malware analysis, preventing zero-day threats. AI-assisted deception technologies create realistic decoy environments, tricking attackers and gathering intelligence on their methods. SOC teams use AI-powered threat hunting to detect and contain advanced persistent threats (APTs), demonstrating AI’s increasing role in proactive cyber defense.
How do you see generative AI evolving in the cybersecurity domain over the next few years?
Generative AI is expected to evolve towards more autonomous threat detection, real-time risk assessments, and AI-driven security automation. Advanced AI models will leverage federated learning to improve accuracy while maintaining privacy. The cybersecurity landscape will witness an ongoing arms race between adversarial AI and AI-driven defense mechanisms. Regulatory bodies will enforce stricter AI governance frameworks to ensure responsible AI deployment. AI-powered digital forensics will enhance post-incident investigations, while AI-driven SOCs will redefine the speed and efficiency of cybersecurity operations.
What role does human oversight (HITL) play in ensuring generative AI systems are effectively managing cybersecurity threats?
Human-in-the-loop (HITL) oversight is essential in cybersecurity to validate AI-generated intelligence, mitigate false positives, and ensure ethical AI deployment. While AI enhances automation, human expertise remains crucial in interpreting complex cyber threats, refining AI models, and making critical security decisions. Cybersecurity professionals audit AI-driven alerts to prevent misclassifications and improve threat detection accuracy. HITL also helps counter adversarial AI attacks by continuously refining AI training datasets with real-world cyber incidents, strengthening overall security resilience.
How can smaller organizations with limited budgets incorporate generative AI for cybersecurity?
Smaller organizations can adopt AI-driven cybersecurity solutions through cloud-based security services, AI-enhanced threat intelligence platforms, and open-source AI tools. Managed Security Service Providers (MSSPs) offer AI-powered SOC capabilities, reducing the need for in-house expertise. AI-driven endpoint protection solutions and automated phishing detection tools provide affordable and effective cybersecurity measures. Prioritizing AI-enhanced automation enables smaller organizations to improve their security posture while minimizing operational costs.
What best practices would you recommend for implementing generative AI tools while minimizing risks?
Organizations should follow best practices such as establishing AI security frameworks, ensuring transparency in AI models, and mitigating adversarial threats. Regular audits of AI-driven threat intelligence improve accuracy and reliability. Human oversight remains crucial for validating AI-generated security alerts. Security teams should conduct adversarial testing and red teaming exercises to assess AI model vulnerabilities. Compliance with industry regulations, such as GDPR and NIST AI risk management guidelines, ensures responsible AI adoption. Implementing strict data governance policies strengthens AI security and trustworthiness in cybersecurity operations.
Artificial Intelligence
Cequence Intros Security Layer to Protect Agentic AI Interactions

Cequence Security has announced significant enhancements to its Unified API Protection (UAP) platform to deliver a comprehensive security solution for agentic AI development, usage, and connectivity. This enhancement empowers organizations to secure every AI agent interaction, regardless of the development framework. By implementing robust guardrails, the solution protects both enterprise-hosted AI applications and external AI APIs, preventing sensitive data exfiltration through business logic abuse and ensuring regulatory compliance.
There is no AI without APIs, and the rapid growth of agentic AI applications has amplified concerns about securing sensitive data during their interactions. These AI-driven exchanges can inadvertently expose internal systems, create significant vulnerabilities, and jeopardize valuable data assets. Recognising this critical challenge, Cequence has expanded its UAP platform, introducing an enhanced security layer to govern interactions between AI agents and backend services specifically. This new layer of security enables customers to detect and prevent AI bots such as ChatGPT from OpenAI and Perplexity from harvesting organizational data.
Internal telemetry across Global 2000 deployments shows that the overwhelming majority of AI-related bot traffic, nearly 88%, originates from large language model infrastructure, with most requests obfuscated behind generic or unidentified user agents. Less than 4% of this traffic is transparently attributed to bots like GPTBot or Gemini. Over 97% of it comes from U.S.-based IP addresses, highlighting the concentration of risk in North American enterprises. Cequence’s ability to detect and govern this traffic in real time, despite the lack of clear identifiers, reinforces the platform’s unmatched readiness for securing agentic AI in the wild.
Key enhancements to Cequence’s UAP platform include:
- Block unauthorized AI data harvesting: Understanding that external AI often seeks to learn by broadly collecting data without obtaining permission, Cequence provides organizations with the critical capability to manage which AI, if any, can interact with their proprietary information.
- Detect and prevent sensitive data exposure: Empowers organizations to effectively detect and prevent sensitive data exposure across all forms of agentic AI. This includes safeguarding against external AI harvesting attempts and securing data within internal AI applications. The platform’s intelligent analysis automatically differentiates between legitimate data access during normal application usage and anomalous activities signaling sensitive data exfiltration, ensuring comprehensive protection against AI-related data loss.
- Discover and manage shadow AI: Automatically discovers and classifies APIs from agentic AI tools like Microsoft Copilot and Salesforce Agentforce, presenting a unified view alongside customers’ internal and third-party APIs. This comprehensive visibility empowers organizations to easily manage these interactions and effectively detect and block sensitive data leaks, whether from external AI harvesting or internal AI usage.
- Seamless integration: Integrates easily into DevOps frameworks for discovering internal AI applications and generates OpenAPI specifications that detail API schemas and security mechanisms, including strong authentication and security policies. Cequence delivers powerful protection without relying on third-party tools, while seamlessly integrating with the customer’s existing cybersecurity ecosystem. This simplifies management and security enforcement.
“Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. We’ve taken immediate action to extend our market-leading API security and bot management capabilities,” said Ameya Talwalkar, CEO of Cequence. “Agentic AI introduces a new layer of complexity, where every agent behaves like a bidirectional API. That’s our wheelhouse. Our platform helps organizations embrace innovation at scale without sacrificing governance, compliance, or control.”
These extended capabilities will be generally available in June.
Artificial Intelligence
Fortinet Expands FortiAI Across its Security Fabric Platform

Fortinet has announced major upgrades to FortiAI, integrating advanced AI capabilities across its Security Fabric platform to combat evolving threats, automate security tasks, and protect AI systems from cyber risks. As cybercriminals increasingly weaponize AI to launch sophisticated attacks, organizations need smarter defenses. Fortinet—with 500+ AI patents and 15 years of AI innovation—now embeds FortiAI across its platform to:
- Stop AI-powered threats
- Automate security and network operations
- Secure AI tools used by businesses
“Fortinet’s AI advantage stems from the breadth and depth of our AI ecosystem—shaped by over a decade of AI innovation and reinforced by more patents than any other cybersecurity vendor,” said Michael Xie, Founder, President, and Chief Technology Officer at Fortinet. “By embedding FortiAI across the Fortinet Security Fabric platform, including new agentic AI capabilities, we’re empowering our customers to reduce the workload on their security and network analysts while improving the efficiency, speed, and accuracy of their security and networking operations. In parallel, we’ve added coverage across the Fabric ecosystem to enable customers to monitor and control the use of GenAI-enabled services within their organization.”
Key upgrades:
FortiAI-Assist – AI That Works for You
- Automatic Network Fixes: AI configures, validates, and troubleshoots network issues without human help.
- Smarter Security Alerts: Cuts through noise, prioritizing only critical threats.
- AI-Powered Threat Hunting: Scans for hidden risks and traces attack origins.
FortiAI-Protect – Defending Against AI Threats
- Tracks 6,500+ AI apps, blocking risky or unauthorized usage.
- Stops new malware with machine learning.
- Adapts to new attack methods in real time.
FortiAI-SecureAI – Safe AI Adoption
- Protects AI models, data, and cloud workloads.
- Prevents leaks from tools like ChatGPT.
- Enforces zero-trust access for AI systems.
FortiAI processes queries locally, ensuring sensitive data never leaves your network.
Artificial Intelligence
SandboxAQ Platform Tackles AI Agent “Non-Human Identity” Threats

SandboxAQ has announced the general availability of AQtive Guard, a platform designed to secure Non-Human Identities (NHIs) and cryptographic assets. This critical security solution arrives as organizations worldwide face increasingly sophisticated AI-driven threats capable of autonomously infiltrating networks, bypassing traditional defenses, and exploiting vulnerabilities at machine speed.
Modern enterprises are experiencing an unprecedented surge in machine-to-machine communications, with billions of AI agents now operating across corporate networks. These digital entities – ranging from legitimate automation tools to potential attack vectors – depend on cryptographic keys, digital certificates, and machine identities that frequently go unmanaged. This oversight creates massive security gaps that malicious actors can exploit, leading to potential data breaches, compliance violations, and operational disruptions.
“There will be more than one billion AI agents with significant autonomous power in the next few years,” stated Jack Hidary, CEO of SandboxAQ. “Enterprises are giving AI agents a vastly increased range of capabilities to impact customers and real-world assets. This creates a dangerous attack surface for adversaries. AQtive Guard’s Discover and Protect modules address this urgent issue.”
AQtive Guard addresses these challenges through its integrated Discover and Protect modules. The Discover component maintains continuous, real-time visibility into all NHIs and cryptographic assets including keys, certificates, and algorithms – a fundamental requirement for maintaining regulatory compliance. The Protect module then automates critical security workflows, enforcing essential policies like automated credential rotation and certificate renewal to proactively mitigate risks before they can be exploited.
At the core of AQtive Guard’s capabilities are SandboxAQ’s industry-leading Large Quantitative Models (LQMs), which provide organizations with unmatched visibility and control over their cryptographic infrastructure. This advanced technology enables enterprises to successfully navigate evolving security standards, including the latest NIST requirements, while maintaining robust protection against emerging threats.

Marc Manzano, General Manager of Cybersecurity at SandboxAQ
“As organizations accelerate AI adoption and the use of agents and machine-to-machine communication across all business domains and functions, maintaining a real-time, accurate inventory of NHIs and cryptographic assets is an essential cybersecurity practice. Being able to automatically remediate vulnerabilities and policy violations identified is crucial to decrease time to mitigation and prevent potential breaches within the first day of use of our software,” said Marc Manzano, General Manager of Cybersecurity at SandboxAQ.
SandboxAQ has significantly strengthened AQtive Guard’s capabilities through deep technical integrations with two cybersecurity industry leaders. The platform now features robust integration with CrowdStrike’s Falcon® platform, enabling direct ingestion of endpoint data for real-time vulnerability detection and immediate one-click remediation. This seamless connection allows security teams to identify and neutralize threats with unprecedented speed.
Additionally, AQtive Guard now offers full interoperability with Palo Alto Networks’ security solutions. By analyzing and incorporating firewall log data, the platform delivers enhanced network visibility, improved threat detection, and stronger compliance with enterprise security policies across hybrid environments.
AQtive Guard delivers a comprehensive, AI-powered approach to managing NHIs and cryptographic assets through four key functional areas. The platform’s advanced vulnerability detection system aggregates data from multiple sources including major cloud providers like AWS and Google Cloud, maintaining a continuously updated inventory of all cryptographic assets.
The solution’s AI-driven risk analysis engine leverages SandboxAQ’s proprietary Cyber LQMs to accurately prioritize threats while dramatically reducing false positives. This capability is enhanced by an integrated GenAI assistant that helps security teams navigate complex compliance requirements and implement appropriate remediation strategies.
For operational efficiency, AQtive Guard automates the entire lifecycle management of cryptographic assets, including issuance, rotation, and revocation processes. This automation significantly reduces manual errors while eliminating the risks associated with stale or compromised credentials. The platform also provides robust compliance support with pre-configured rulesets for major regulatory standards, customizable query capabilities, and comprehensive reporting features. These tools help organizations accelerate their transition to new NIST standards while maintaining continuous compliance with evolving requirements.
Available now as a fully managed, cloud-native solution, AQtive Guard is designed for rapid deployment and immediate impact. Enterprises can register for priority access to begin early adoption and conduct comprehensive risk assessments of their cryptographic infrastructure.
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