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Proactive Cybersecurity: Leveraging Generative AI for Early Threat Detection

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Craig Sanderson, Vice President of Security Products at Infoblox, emphasises the importance of leveraging DNS-layer security to prevent cyber threats. He advocates for integrating advanced threat detection tools and automating security operations to enhance efficiency and protect enterprise networks

How is generative AI being utilised to enhance cybersecurity measures today?
Generative AI is being used to enhance cybersecurity by analysing vast amounts of network telemetry data to identify patterns and anomalies that indicate potential threats. For example, AI can analyse DNS data to detect and block malicious domains before they become harmful. AI-driven cybersecurity tools can predict and neutralise threats by profiling threat actor behavior and identifying suspicious activities early on.

What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
Generative AI introduces several risks in the cybersecurity landscape. It enables attackers to create more sophisticated and convincing phishing campaigns and deepfakes, making it harder for victims to detect fraud. AI also lowers the skill barrier for launching cyberattacks, allowing less experienced threat actors to develop and execute complex attacks. Additionally, AI increases the scalability of cybercriminal operations, enabling the rapid creation of large volumes of malware and scam campaigns.

How can organisations leverage generative AI for proactive threat detection and response?
Organisations can leverage generative AI for proactive threat detection and response in several ways. AI can be used to analyse DNS and network telemetry data, helping to uncover hidden patterns and predict attacks before they happen. It can also profile threat actors by continuously learning from network activity to identify suspicious patterns and neutralise threats early. Additionally, AI-driven tools can automate threat detection and response, significantly reducing the time required to identify and mitigate potential threats.

What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
Ethical concerns when using generative AI in cybersecurity include issues related to bias and fairness, privacy, and accountability. It is important to ensure that AI algorithms do not introduce bias in threat detection and response. Additionally, there must be a balance between the need for data analysis and the protection of individual privacy. Clear accountability for decisions made by AI systems is also essential. These concerns can be addressed by implementing transparent AI practices, conducting regular audits, and involving human oversight in critical decision-making processes.

What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Challenges in using AI for cybersecurity include ensuring the quality and relevance of the data used for analysis, addressing the skill gaps in AI and machine learning within cybersecurity teams, and seamlessly integrating AI tools with existing cybersecurity infrastructure.

Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
Yes, a notable example is the use of AI-powered DNS defenses that identified and blocked malicious domains associated with the ransomware group BlackCat 163 days before they appeared in commercial or public intelligence feeds. This proactive approach prevented threats from reaching “patient zero,” significantly reducing the risk of breaches and data exfiltration.

How do you see generative AI evolving in the cybersecurity domain over the next few years?
Generative AI is expected to evolve by enhancing predictive capabilities, improving the ability to predict and neutralise emerging threats. It will also integrate with Zero Trust Architectures, strengthening security by ensuring that only verified interactions occur within networks. Additionally, it will help reduce alert fatigue by streamlining threat detection and response processes, alleviating the pressure on Security Operations Centers (SOCs).

What role does human oversight (HITL) play in ensuring generative AI systems are effectively managing cybersecurity threats?
Human oversight is crucial in ensuring generative AI systems effectively manage cybersecurity threats by validating AI-generated insights and actions, addressing complex and nuanced threats that AI may not fully understand, and maintaining accountability for decisions made by AI systems.

How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
Smaller organisations can incorporate generative AI for cybersecurity by leveraging cloud-based solutions that offer scalable and cost-effective security tools, partnering with Managed Security Service Providers (MSSPs) that provide AI-driven cybersecurity services, and focusing on high-impact areas, such as DNS security, to prioritise AI implementation.

What best practices would you recommend for implementing generative AI tools while minimising risks?
Best practices include ensuring data quality by using high-quality and relevant data for AI analysis, maintaining transparency through the implementation of transparent AI practices and regular audits, involving human oversight in critical decision-making processes, and seamlessly integrating AI tools with existing cybersecurity infrastructure.

Artificial Intelligence

CyberKnight Partners with Ridge Security for AI-Powered Security Validation

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The automated penetration testing market was valued at roughly $3.1 billion in 2023 and is projected to grow rapidly, with forecasts estimating a compound annual growth rate (CAGR) between 21% and 25%. By 2030, the sector is expected to reach approximately $9 to $10 billion. The broader penetration testing industry is also expanding, with projections indicating it will surpass $5.3 billion by 2027, according to MarketandMarket.

To support enterprises and government entities across the Middle East, Turkey and Africa (META) with identifying and validating vulnerabilities and reducing security gaps in real-time, CyberKnight has partnered with Ridge Security, the World’s First Al-powered Offensive Security Validation Platform. Ridge Security’s products incorporate advanced artificial intelligence to deliver security validation through automated penetration testing and breach and attack simulations.

RidgeBot uses advanced AI to autonomously perform multi-vector iterative attacks, conduct continuous penetration testing, and validate vulnerabilities with zero false positives. RidgeBot has been deployed by customers worldwide as a key element of their journey to evolve from traditional vulnerability management to Continuous Threat Exposure Management (CTEM).

“Ridge Security’s core strength lies in delivering holistic, AI-driven security validation that enables organizations to proactively manage risk and improve operational performance,” said Hom Bahmanyar, Chief Enablement Officer at Ridge Security. “We are delighted to partner with CyberKnight to leverage their network of strategic partners, deep-rooted customer relations, and security expertise to accelerate our expansion plans in the region.”

“Our partnership with Ridge Security is a timely and strategic step, as 69% of organizations are now adopting AI-driven security for threat detection and prevention,” added Wael Jaber, Chief Strategy Officer at CyberKnight. “By joining forces, we enhance our ability to deliver automated, intelligent security validation solutions, reaffirming our commitment to empowering customers with resilient, future-ready cybersecurity across the region.”

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Cequence Intros Security Layer to Protect Agentic AI Interactions

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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.

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Fortinet Expands FortiAI Across its Security Fabric Platform

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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

  1. Automatic Network Fixes: AI configures, validates, and troubleshoots network issues without human help.
  2. Smarter Security Alerts: Cuts through noise, prioritizing only critical threats.
  3. AI-Powered Threat Hunting: Scans for hidden risks and traces attack origins.

FortiAI-Protect – Defending Against AI Threats

  1. Tracks 6,500+ AI apps, blocking risky or unauthorized usage.
  2. Stops new malware with machine learning.
  3. Adapts to new attack methods in real time.

FortiAI-SecureAI – Safe AI Adoption

  1. Protects AI models, data, and cloud workloads.
  2. Prevents leaks from tools like ChatGPT.
  3. Enforces zero-trust access for AI systems.

FortiAI processes queries locally, ensuring sensitive data never leaves your network.

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