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AI Will Shift Cybersecurity From Reactive to Predictive Only If Deployed Responsibly

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Generative AI isn’t just a tool—it’s a game-changer for cybersecurity, says Saif Alrefai, Solutions Engineering Manager at OPSWAT. But without the right strategy, it can also become a weapon for attackers

How is generative AI being utilised to enhance cybersecurity measures today?
Generative AI enhances cybersecurity by improving threat detection, accelerating incident response, and optimising security operations. AI models analyse vast datasets to predict and identify threats before they materialise. They can simulate realistic attacks to assess vulnerabilities and strengthen defenses. Additionally, AI-powered tools help detect phishing attempts, malware, and other cyber threats, reducing the workload for cybersecurity teams and improving overall efficiency.

What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
Generative AI poses significant risks, especially when exploited by cybercriminals. AI-driven attacks can generate highly convincing phishing emails, undetectable malware, and automated vulnerability exploitation at an unprecedented scale. Moreover, AI-powered threats can evolve in real-time, making them more challenging to counter and requiring continuous adaptation from security teams.

How can organisations leverage generative AI for proactive threat detection and response?
Organisations can use generative AI to identify patterns in network traffic, user behavior, and system activities to detect anomalies and potential threats early. AI-driven attack simulations can help expose vulnerabilities before they are exploited. Additionally, AI can automate responses by isolating compromised systems or blocking malicious traffic, significantly reducing reaction time and mitigating damage.

What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
A major ethical concern is the use of generative AI by malicious actors to create undetectable malware, convincing phishing campaigns, and deepfakes, which pose serious cybersecurity risks. Another issue is potential bias in AI models, leading to unfair targeting or misclassification. Addressing these concerns requires diverse and unbiased training data, regular audits, and clear regulatory frameworks to ensure AI is deployed transparently and responsibly.

What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Integrating generative AI into cybersecurity workflows comes with challenges such as the complexity of AI models, the need for ongoing training, and the decision between investing in AI infrastructure or outsourcing. Ensuring seamless integration with existing security tools requires thorough testing. Additionally, AI-generated decisions may not always be explainable, making it difficult for professionals to fully trust automated outcomes without human verification.

Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
Yes, generative AI has been successfully used in red teaming exercises to simulate cyberattacks, helping organisations identify weaknesses before they are exploited. It has also played a role in advanced malware detection by identifying unknown malware strains through pattern recognition. AI-powered security systems have even been able to predict ransomware attacks based on historical data and block suspicious activities before they cause harm.

How do you see generative AI evolving in the cybersecurity domain over the next few years?
Generative AI is expected to become more specialised, providing advanced, adaptive defense mechanisms. It will enhance predictive capabilities against threats such as zero-day exploits and advanced persistent threats, shifting cybersecurity from reactive to proactive. AI-driven security orchestration will automate incident response across hybrid and multi-cloud environments. As AI continues to learn from real-time data, it may become more autonomous, requiring less human intervention while improving threat mitigation.

What role does human oversight (HITL) play in ensuring generative AI systems are effectively managing cybersecurity threats?
Human oversight remains crucial to ensure AI-driven cybersecurity systems function effectively. While AI excels at processing large data volumes and detecting anomalies, human experts provide context, validate AI-generated insights, and make critical decisions in complex scenarios. Human involvement also helps prevent AI biases, detect adversarial manipulation, and fine-tune models to adapt to emerging threats.

How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
Smaller organisations can adopt generative AI through cost-effective solutions such as cloud-based AI security tools and AI-as-a-Service platforms, which eliminate the need for heavy infrastructure investment. These solutions offer AI-driven threat detection and response without requiring extensive resources. Open-source AI models and partnerships with managed security service providers (MSSPs) can also help small businesses implement AI-driven cybersecurity measures tailored to their needs.

What best practices would you recommend for implementing generative AI tools while minimising risks?
To minimise risks, organisations should:

  1. Train AI models on diverse, unbiased datasets.
  2. Conduct regular audits to detect ethical and functional issues.
  3. Maintain human oversight for critical decisions.
  4. Ensure AI systems provide explainable and transparent outputs.
  5. Test AI tools in controlled environments before full deployment.
  6. Establish clear policies on ethical AI use, data privacy, and regulatory compliance.

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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Artificial Intelligence

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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Artificial Intelligence

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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