Artificial Intelligence
Generative AI in Cybersecurity: Opportunities, Risks, and the Road Ahead

Rob T. Lee, Chief of Research at the SANS Institute, offers deep insights into the transformative role of generative AI in cybersecurity. With its ability to streamline workflows and enhance threat detection, generative AI is proving to be a game-changer, though it comes with significant challenges like privacy concerns and the ever-accelerating pace of innovation in the field
How is generative AI being utilised to enhance cybersecurity measures today?
Generative AI is being used in nearly every security workflow from digital forensics detection and vulnerability assessments.
What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
Most of these models are not audited and therefore businesses are increasing the risk because security guidelines do not directly exist to ensure the security of the newer models.
How can organisations leverage generative AI for proactive threat detection and response?
First, LLMs love large data sets, and it is now possible to ingest more network, logfile, and EDR data into the LLMs, becoming an SIEM on steroids. Second, with a combination of reasoning in addition to proper cyber threat intelligence, new attack paths can be identified even if unknown TTPs. It will become a game changer. Entities such as Wiz have already proven that AI combined with proper monitoring is a game changer.
What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
The biggest issue is the privacy-related ingestion of data. Many cybersecurity applications require access to emails, websites, system content, network traffic attributable to specific users, and more. The adversaries could survive longer without cybersecurity capabilities to monitor these key artifacts. Data protection on privacy is key.
What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
There aren’t enough individuals who research the art of the possible. Individuals must pay attention to capabilities as things move at lightning speed. Only 38 days or around 900 hours between DeepSeek release and Manus following for advanced reasoning using tools. Waiting for someone else to solve how to improve workflows is dangerous and requires consistent learning daily, and assigning team leads requires the sharing of new techniques among team members. Too many are not paying attention to how fast AI is moving or what the potential impacts are.
Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
AI workflow enhancement has increased the velocity at which organisations can move. I hear of new examples daily of how AI has enabled teams to move more quickly to remediate and respond to events. Like working out – your gains can be seen over a longer period, but a 1% improvement a week is what I advise organisations trying to implement AI.
How do you see generative AI evolving in the cybersecurity domain over the next few years?
The offensive will likely gain the upper hand as they will be using AI unrestricted, while legislation, safety, ethics, and privacy concerns might inhibit the defensive. A debate must occur to maintain eye contact with the ever-increasing velocity of the advanced adversary teams, and it is very concerning that I foresee that not enough leadership understand the cybersecurity implications of data protection in AI, even though I equally fully agree with the concerns. Innovation must be unleashed, but there might be privacy restrictions reduction to achieve this.
How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
People. Smart people. Who understand that they need to 10X their workflows. This is where a trained individual can completely become superhuman if they learn AI capabilities and try new ideas in their daily workflows. Small companies can completely do more with less people or without needing to hire new FTE to save on costs.
What best practices would you recommend for implementing generative AI tools while minimising risks?
The same policies in place as it is with the internet. Nothing changes just because a new technology is released. I cannot think of a single recommended security awareness recommendation that is not transferable to AI. It is no different than cloud, or mobile, etc. It is just a different tool, but the same thought process should exist. Just because you are in a new country that drives on the left side of the road doesn’t mean you don’t look both ways before you cross the street.
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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