Artificial Intelligence
Generative AI Redefining Cybersecurity with Advanced Capabilities
Emad Fahmy, Systems Engineering Director at NETSCOUT, emphasizes the importance of leveraging advanced threat analytics and adaptive DDoS solutions to address the evolving cybersecurity challenges in hybrid environments. He highlights NETSCOUT’s commitment to providing real-time visibility, actionable insights, and innovative technologies to enhance network security and resilience against sophisticated cyber threats
How is generative AI being utilised to enhance cybersecurity measures today?
Generative AI is improving cybersecurity by helping detect and stop threats more effectively. It can recognise patterns in cyberattacks, like malware and unusual network activity, that traditional security systems might miss. AI also speeds up response times by automatically taking action against threats. Additionally, it helps businesses manage risks by tracking security gaps and ensuring compliance with safety rules. While AI strengthens security, it also brings new challenges, such as the risk of AI-generated attacks and concerns about data privacy, making careful use important.
What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
Generative AI can make some cyberattacks more effective and harder to detect. AI-powered social engineering enables extremely convincing phishing emails to be created, can mimic real voices and generate deepfake images that may bypass biometric security. AI also scales cyberattacks more efficiently, optimising DDoS, credential stuffing and malware deployment. In Unified Communications as a Service (UCaaS) platforms, AI automation introduces new risks, as AI-generated text and responses could spread misinformation.
How can organisations leverage generative AI for proactive threat detection and response?
Organisations can use AI-driven systems to automate threat detection and response. This means analysing network data in real time, identifying anomalies at speed and detecting attack patterns before breaches occur. AI also helps counter social engineering by recognising phishing attempts and deepfake content. Additionally, AI-powered tools can automate attack resolution processes, improving speed and accuracy. However, it’s important to remember that human oversight and trained cybersecurity teams are essential to interpret AI insights and mitigate risks effectively. A combination of AI-driven defence and human oversight is the best strategy for organisations to stay ahead of evolving cyber threats.
What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
Ethical concerns around generative AI in cybersecurity arise from both its misuse by attackers and the risks associated with AI-driven defences. Cybercriminals can harness the power of AI for more sophisticated phishing, deepfake manipulation and large-scale automated attacks, raising concerns about privacy, misinformation and identity fraud. While AI strengthens security, over-reliance on automation can also lead to false positives or missed threats if not properly monitored. To mitigate these risks, organisations must combine AI-driven cybersecurity with human oversight. Training cybersecurity teams, ensuring responsible threat detection and maintaining transparency in AI decision-making are essential for ethical and effective cybersecurity.
What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Cybersecurity teams face several hurdles when adopting generative AI. A major issue is practicality, as many AI initiatives sound promising but lack clear, actionable solutions. Workforce automation is another concern, as ongoing labour shortages continue to stretch security teams. While AI has been around for decades, much of today’s focus is on large-scale models rather than targeted, practical applications. Smaller AI/ML projects that are quicker and more cost-effective to deploy may offer a better approach. However, the current AI hype makes it difficult to distinguish real innovation from inflated expectations, further complicating integration efforts.
How do you see generative AI evolving in the cybersecurity domain over the next few years?
Generative AI is set to play an increasingly complex role in cybersecurity over the next few years. Cybercriminals are already leveraging it to automate phishing attacks, generate deepfake scams, and optimise large-scale threats like DDoS attacks. As AI technology advances, these threats will become more sophisticated and harder to detect. Conversely, AI-driven security tools will evolve to counteract these risks by enhancing threat detection, improving anomaly detection, and accelerating response times. While AI will improve cybersecurity defences, its success will depend on balancing automation with human expertise to prevent the misidentification of threats.
What role does human oversight play in ensuring generative AI systems are effectively managing cybersecurity threats?
AI is a powerful asset in cybersecurity, but it’s not infallible. That’s where human oversight is critical. AI can rapidly detect threats and automate responses, but it lacks contextual understanding and can misinterpret data, leading to false positives. Security teams must stay engaged, validating AI-driven insights, refining models and ensuring decision accuracy. Generative AI still struggles with reliability, making expert involvement essential to prevent costly mistakes and build trust. The most effective approach combines AI’s speed with human judgement, creating smarter, more resilient cybersecurity operations.
How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
Smaller organisations can adopt generative AI for cybersecurity by leveraging cost-effective, cloud-based AI-driven security solutions. Instead of investing in expensive in-house AI models, they can use AIOps platforms that automate threat detection and incident response, delivering actionable insights without requiring large security teams. AI-powered monitoring tools can also help identify security risks proactively, reducing response times. However, human oversight remains essential—AI is most effective when combined with expert analysis. By strategically integrating AI with human intelligence, smaller organisations can strengthen their security without exceeding their budgets.
What best practices would you recommend for implementing generative AI tools while minimising risks?
Implementing generative AI in cybersecurity requires a careful balance of automation and human oversight. AI should generate reliable, predictable results rather than depending on large language models that may introduce inaccuracies. Continuous monitoring is essential to prevent AI from mistakenly blocking legitimate traffic or disrupting operations. Organisations should leverage AI for real-time threat detection while keeping human experts involved in critical decision-making.
Artificial Intelligence
CyberKnight Partners with Ridge Security for AI-Powered Security Validation
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.”
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.
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