Artificial Intelligence
AI Will Introduce New Threats as LLMs Take Over Automated Systems

Chester Wisniewski, the Director and Global field CTO at Sophos, says, criminals are using AI for exclusively for social scams and the social aspects of traditional attacks
How is generative AI being utilised to enhance cybersecurity measures today?
AI brings a wide variety of advantages to cybersecurity: automation, speed, scalability, enhanced detection, and generalisability. Without AI, rule-based systems need immense manual upkeep to handle the scale of modern threats. AI models can generalise by learning relationships between any number of potentially hundreds of features, while human analysts cannot write such complex rules. AI does, however, stand to introduce new threats as large language models take over automated systems.
What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
For the most part, criminals are using AI for exclusively for social scams and the social aspects of traditional attacks. AI allows for accurate translation at scale, which dramatically increases the quality of social scams. It can also be used to create high-quality phishing emails that are indistinguishable from the real thing.
AI chatbots are also very useful for initiating conversations with potential victims and setting the hook. Once a victim has been captured, humans usually take over but can still use AI to help with translation and grammar. One additional area where AI might be useful is in assessing the value of large volumes of stolen data. Using AI a criminal might be quicker to identify high-value data and either sell it at a premium or use it as an extra pressure mechanism against the victim.
What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
In most applications there aren’t many ethical concerns. Clearly using AI to generate malicious code or to gather open source intelligence should be done with caution, but most cybersecurity applications don’t involve many ethical dilemmas.
What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Two primary concerns come to mind. First is when you are using generative to help you write code you must do very thorough reviews to be sure you are not introducing vulnerabilities. Generative AI has been known to make up the names of libraries that don’t exist or recommend code snippets containing basic programming mistakes like allowing SQL injection attacks or buffer overflow attacks. Second, we must verify the outputs when it really matters. Frequently mild inaccuracies may not matter, but when in circumstances where it is of great importance, we have to double-check the outputs to ensure the accuracy of the results.
Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
Not that I am aware of. Traditional machine learning and neural-network malware detection models prevent attacks around the clock, but I am not aware of generative AI being used in this way to date.
How do you see generative AI evolving in the cybersecurity domain over the next few years?
I think the real promise is in alert triage and language translation capabilities. Of course, these technologies are available now from ourselves and other vendors, but as these capabilities mature, they will become increasingly important for smart automation and aiding human analysts. We are also likely to see AI automation of bug discovery in code before it ships to customers preventing vulnerabilities and improved detection of targeted phishing attacks in email solutions.
What role does human oversight (HITL) play in ensuring generative AI systems are effectively managing cybersecurity threats?
This is critically important. The machines are excellent at processing vast amounts of data and helping make sense of it, but they lack intuition, creativity, and context. Humans can take this reduced flow of information and add that intelligence to achieve superior outcomes.
How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
Most smaller organisations will benefit from AI through its integration into their existing tools and through their service providers. Much of the efficiencies gained by smart applications of this technology will allow for more affordable services from security providers and easier to use tools.
What best practices would you recommend for implementing generative AI tools while minimising risks?
If using AI models hosted in public clouds or by service providers caution must be exercised to not process sensitive information using these tools. Risks can be minimised by choosing providers in countries with privacy laws in-line with your responsibilities, but caution should still be exercised. For the most sensitive types of information, it would be best to host it on-premise or in a private cloud instance that is not shared with other tenants.
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.
Artificial Intelligence
SandboxAQ Platform Tackles AI Agent “Non-Human Identity” Threats

SandboxAQ has announced the general availability of AQtive Guard, a platform designed to secure Non-Human Identities (NHIs) and cryptographic assets. This critical security solution arrives as organizations worldwide face increasingly sophisticated AI-driven threats capable of autonomously infiltrating networks, bypassing traditional defenses, and exploiting vulnerabilities at machine speed.
Modern enterprises are experiencing an unprecedented surge in machine-to-machine communications, with billions of AI agents now operating across corporate networks. These digital entities – ranging from legitimate automation tools to potential attack vectors – depend on cryptographic keys, digital certificates, and machine identities that frequently go unmanaged. This oversight creates massive security gaps that malicious actors can exploit, leading to potential data breaches, compliance violations, and operational disruptions.
“There will be more than one billion AI agents with significant autonomous power in the next few years,” stated Jack Hidary, CEO of SandboxAQ. “Enterprises are giving AI agents a vastly increased range of capabilities to impact customers and real-world assets. This creates a dangerous attack surface for adversaries. AQtive Guard’s Discover and Protect modules address this urgent issue.”
AQtive Guard addresses these challenges through its integrated Discover and Protect modules. The Discover component maintains continuous, real-time visibility into all NHIs and cryptographic assets including keys, certificates, and algorithms – a fundamental requirement for maintaining regulatory compliance. The Protect module then automates critical security workflows, enforcing essential policies like automated credential rotation and certificate renewal to proactively mitigate risks before they can be exploited.
At the core of AQtive Guard’s capabilities are SandboxAQ’s industry-leading Large Quantitative Models (LQMs), which provide organizations with unmatched visibility and control over their cryptographic infrastructure. This advanced technology enables enterprises to successfully navigate evolving security standards, including the latest NIST requirements, while maintaining robust protection against emerging threats.

Marc Manzano, General Manager of Cybersecurity at SandboxAQ
“As organizations accelerate AI adoption and the use of agents and machine-to-machine communication across all business domains and functions, maintaining a real-time, accurate inventory of NHIs and cryptographic assets is an essential cybersecurity practice. Being able to automatically remediate vulnerabilities and policy violations identified is crucial to decrease time to mitigation and prevent potential breaches within the first day of use of our software,” said Marc Manzano, General Manager of Cybersecurity at SandboxAQ.
SandboxAQ has significantly strengthened AQtive Guard’s capabilities through deep technical integrations with two cybersecurity industry leaders. The platform now features robust integration with CrowdStrike’s Falcon® platform, enabling direct ingestion of endpoint data for real-time vulnerability detection and immediate one-click remediation. This seamless connection allows security teams to identify and neutralize threats with unprecedented speed.
Additionally, AQtive Guard now offers full interoperability with Palo Alto Networks’ security solutions. By analyzing and incorporating firewall log data, the platform delivers enhanced network visibility, improved threat detection, and stronger compliance with enterprise security policies across hybrid environments.
AQtive Guard delivers a comprehensive, AI-powered approach to managing NHIs and cryptographic assets through four key functional areas. The platform’s advanced vulnerability detection system aggregates data from multiple sources including major cloud providers like AWS and Google Cloud, maintaining a continuously updated inventory of all cryptographic assets.
The solution’s AI-driven risk analysis engine leverages SandboxAQ’s proprietary Cyber LQMs to accurately prioritize threats while dramatically reducing false positives. This capability is enhanced by an integrated GenAI assistant that helps security teams navigate complex compliance requirements and implement appropriate remediation strategies.
For operational efficiency, AQtive Guard automates the entire lifecycle management of cryptographic assets, including issuance, rotation, and revocation processes. This automation significantly reduces manual errors while eliminating the risks associated with stale or compromised credentials. The platform also provides robust compliance support with pre-configured rulesets for major regulatory standards, customizable query capabilities, and comprehensive reporting features. These tools help organizations accelerate their transition to new NIST standards while maintaining continuous compliance with evolving requirements.
Available now as a fully managed, cloud-native solution, AQtive Guard is designed for rapid deployment and immediate impact. Enterprises can register for priority access to begin early adoption and conduct comprehensive risk assessments of their cryptographic infrastructure.
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