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AI in Cybersecurity: Securing Tomorrow Today

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Written by Kamal Nagpal, Senior Director – Middle East, Cloud and Network Services, Nokia

As the digital world grows more complex and hostile, the imperative to keep us safe falls on the cybersecurity and telecom industry. Cybersecurity needs to be proactive and not just reactive. In this regard, Artificial Intelligence (AI) and Machine Learning (ML) have quietly revolutionised how we defend against cyber threats. AI’s innate ability to act, learn, and predict is what embeds it so deeply in cybersecurity.

While countries like the UAE make significant strides in adapting AI into every aspect of nation-building with new measures like the AI Blueprint, creating a system on which progress can be made and one that remains safe and vigilant to external threats is essential and central to all communication service providers.

AI: The Past and Present
Predicting an attack before it happens has always been, in essence, what we do as cybersecurity professionals. AI and Machine Learning (ML) are not new entrants and have been around since before generative AI took the stage. The early 2000s saw the first machine learning systems that were employed to identify and mitigate spam mail. As the threats grew more complex, so did the technologies, going from simple rule-based systems to advanced algorithms capable of predictive analysis and real-time threat detection.

Malicious actors continue to grow and use more sophisticated ransomware. Communication Service Providers across the board increasingly struggle to keep up with the ever-changing nature of threat vectors and 42% of CSP respondents believe that fragmented security tools make it harder for security companies to implement security capabilities across different systems. Cybersecurity is of paramount importance to industries like healthcare and banking that are traditionally associated with handling vast amounts of sensitive data, and with devices on the IoT (Internet of Things).

As our reliance on smart appliances and automobiles grows, so do the threats when accessing personal data. Technology and cybersecurity experts estimate that an average data breach can cost enterprises up to US$4.2 million, and that number is only growing. The biggest threats to staying safe in the cyber world we live in are the lack of security automation, increased threat actors, increasingly sophisticated attacks, a fragmented security landscape, and stringent compliance requirements.

Is AI here to stay?
With its innately high levels of automation and insight, AI trumps traditional cybersecurity methods. Given AI’s ability to process vast amounts of data at large speeds, it can also leverage machine learning algorithms to identify patterns and anomalies that can help mitigate threats in a timeframe that cannot be matched by human operators. Perhaps, one of the greatest contributions of AI and machine learning is its ability to analyze behavior. As it continues to gather and analyze threat information from diverse sources, it provides a sharper and more nuanced defence mechanism. The foresight that AI provides enables organizations to implement preventive measures rather than reactive responses, shifting the paradigm of cybersecurity from defence to prevention.

This isn’t to say that there are no challenges when it comes to leveraging AI and machine learning for cybersecurity. Ensuring that the technology operates within the realms of ethical use while prioritizing privacy will remain one of the greatest challenges we face. Ensuring transparency, fairness, and robustness in AI algorithms will be essential as these technologies continue to evolve.

Another omnipresent threat is that of data poisoning. Commonly regarded as one of the darker facets of the, AI can be weaponized and used to leak sensitive data, offer inaccurate information or even create malicious code. Ensuring a robust data validation process to filter out poisoned data and continuous monitoring through risk assessment is imperative to keep AI models safe for a digitally secure future.

The future success of AI is dependent on continuing to have regulatory environments that enable the use of AI across industries and avoiding a fragmented approach that may hinder its adoption. Regional and global collaboration is also an important aspect of staying safe. Sharing threat intelligence and best practices across borders can enhance the overall security of the region.

The use of AI in cybersecurity is more than just a passing trend. Its future is certainly enmeshed with the future of AI, and if we were to look ahead with the data available today, AI could soon be one of the cornerstones of modern and future cybersecurity strategies. As we stand on the brink of new technological frontiers, can we afford not to invest in AI-driven cybersecurity?

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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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