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Forbes
02-07-2025
- Forbes
Why Large Language Models Are The Future Of Cybersecurity
Karan Alang, principal software engineer at Versa Networks with 25 years of experience in AI, cloud and big data. Cybersecurity today faces a key challenge: It lacks context. Modern threats—advanced persistent threats (APTs), polymorphic malware, insider attacks—don't follow static patterns. They hide in plain sight across massive volumes of unstructured data: logs, alerts, threat feeds, user activity, even emails. Traditional defenses—whether signature-based detection, static rules or first-generation ML models—while effective against known threats, struggle with the scale and complexity of modern attack vectors. They often produce false positives, and their rule-based nature means novel or sophisticated attacks are typically detected only after damage has occurred. Large language models (LLMs) have the capability to change this. Originally built to understand and generate natural language, LLMs like GPT-4, Claude, Gemini and others offer something cybersecurity desperately needs: the ability to read between the lines. They can parse logs like narratives, correlate alerts like analysts and summarize incidents with human-level fluency. But LLMs are more than just smarter tools—they're the foundation of a new kind of AI-augmented defense system. The Six Most Promising Use Cases For LLMs In Cybersecurity LLMs can analyze behavioral baselines across users and devices, identifying subtle deviations that signal insider threats or credential abuse. Unlike rigid anomaly detection models, LLMs have the capability of identifying unknown threats and can reduce false positives significantly. By ingesting log data, incident reports and threat intel, LLMs can autonomously map behaviors to relevant MITRE ATT&CK techniques. This streamlines classification and enhances threat response workflows. LLMs excel at identifying unknown threats by recognizing semantic anomalies and behavioral inconsistencies across diverse data. This makes them well-suited for detecting zero-days, novel malware or multistage attack chains with no prior signature. Phishing remains the most common initial attack vector. LLMs can parse email language, structure and embedded content to detect social engineering cues, flagging threats that evade traditional filters. Security operations centers (SOCs) are drowning in alerts. LLMs can act as AI copilots, prioritizing the most relevant incidents, summarizing them in plain English and reducing analyst fatigue. LLMs can digest unstructured threat intelligence—white papers, PDFs, X feeds—and convert them into structured indicators of compromise (IOCs) or STIX/TAXII format for machine consumption. How To Ensure LLM Accuracy: Avoiding Hallucinations In cybersecurity, an incorrect AI-generated response isn't a bug—it's a liability. LLM hallucinations must be proactively mitigated. Here's how to do it right: • Retrieval-Augmented Generation (RAG): Pair the LLM with real-time data sources (logs, threat feeds, MITRE documentation). The model then generates answers based on verified content, not just memory. • Structured Prompting: Use defined templates that limit open-ended generation (e.g., {"mitre_technique": "T1566.001", "confidence": 0.93}). • Human-In-The-Loop Validation: Analysts should review and approve high-impact outputs (e.g., containment actions, incident classification). • Audit Logging: All AI-generated recommendations should be logged, including prompt, retrieved context and final output, for post-incident review and model tuning. • Fine-Tuning + Feedback Loops: Regularly incorporate analyst feedback to improve model accuracy and contextual alignment with your environment. LLMs should not replace your SOC—they should augment it with intelligence that's explainable, traceable and verifiable. Future Outlook: Agentic AI, MCP And Agent-To-Agent Architectures LLMs are the starting point. The next generation of AI in cybersecurity will be built on three converging frontiers: Agentic systems are LLM-powered entities that can reason, plan and take action with constraints. In security, they might: They won't replace analysts—but they'll act like Tier-1 analysts on autopilot, freeing humans for more strategic work. As enterprises deploy multiple AI models across detection, analysis and response, MCPs will standardize context transfer between models: This is essential for regulated environments that require compliance-ready automation. In early-stage prototypes already used in cyber defense research, multiple specialized AI agents communicate to divide tasks: This modular, collaborative AI ecosystem will redefine cybersecurity architecture—where AI agents act like a fully staffed, scalable SOC team. Granted, these architectures are in the nascent stage, but many companies are already applying these in next-gen cyber platforms and have the potential to become mainstream as protocols, standards and guardrails mature. Final Takeaway: What Security Leaders Should Do Now LLMs are no longer an experiment—they're a strategic imperative. Here's what CISOs, CIOs, CTOs and engineering leaders should consider: Conclusion We're entering an era where AI doesn't just help detect threats—it understands them, explains them and, soon enough, will act on them with human guidance. Large language models are not just the future of cybersecurity—they're the context engine that makes the rest of your security stack smarter. Now is the time to invest—not just in the technology but in the architecture and governance needed to make it secure, reliable and impactful. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
01-07-2025
- Business
- Forbes
Selecting The Right GenAI Tool For Sales Productivity
Mickey Singh is a senior leader at Versa Networks, renowned for his proven track record in commercializing emerging technologies. The rise of artificial intelligence has sparked a gold rush across industries, and sales productivity is no exception. As the Global Head of Sales Enablement, I'm constantly fielding calls, emails and LinkedIn messages about the latest AI tools promising to revolutionize sales. Sound familiar? From established sales tech giants to agile startups, everyone's touting AI. But amid the noise, one critical question remains: Are these tools actually effective? Let's unpack what's really going on. Generative AI: Potential Meets Practicality Generative AI (GenAI) uses machine learning to create content—emails, presentations, code and more. It's evolving fast and can solve real business problems—if it's trained on the right data. And that's the catch: Data is everything. Without high-quality, relevant data, even the most advanced AI is like bamboo: hollow and fragile. Why Customization Matters In Sales AI This is especially true for AI-driven sales productivity tools. Off-the-shelf models often fall short because they aren't tailored to your organization's unique sales processes, methodologies and buyer journeys. To be truly effective, AI must be trained and fine-tuned to reflect your workflows. The best tools don't just plug in; they adapt. They learn your language, your cadence and your customer behavior. That level of customization is what separates impactful AI solutions from generic ones. 'We Have AI, Too!' Today, nearly every vendor in CRM, sales performance management and conversation intelligence claims to use AI. It's become a standard part of the pitch: 'AI-driven platform.' Take conversation intelligence, for example. Many tools record customer calls and generate transcripts. But here's the truth: Volume alone doesn't equal value. Just having a massive archive of call recordings and transcripts isn't enough. The old saying 'garbage in, garbage out' still holds. Without meaningful analysis, these tools risk becoming little more than a graveyard of conversations. The real value lies in extracting predictive insights and delivering actionable recommendations that improve sales outcomes. What Makes A Good Sales GenAI Tool? A successful GenAI tool must be deeply trained on your unique sales process, value propositions, product positioning, sales methodologies, buyer's journey and GTM strategies for each product line. The AI algorithms need to be trained on the right data to generate valuable insights and actionable outcomes. It's not just about data volume—it's about data quality. And here's the point: Not all sales GenAI tools are created equal. Slapping an 'AI' label on your tech stack won't fix a broken sales process. There's no magic switch. Before deploying any GenAI tool, it's essential to reassess and streamline your sales process. AI can enhance what already works, but it can't fix what's fundamentally flawed. Human Insight Still Matters AI doesn't replace the need for human input. We still need people to define the sales process, configure business requirements and articulate desired outcomes. Simply feeding a list of discovery call questions won't cut it. Why? Because every sales conversation is unique. It evolves based on the customer's responses, needs and goals. Generic tools with rigid templates often fall short, producing mountains of transcripts but offering little insight. Many sales enablement and operations teams are learning this the hard way: Without flexibility and deep customization, AI tools become just another source of noise. What To Look For In A Sales GenAI Tool Having worked extensively with GenAI-powered sales productivity tools, I've seen how transformative they can be when implemented thoughtfully. I have noticed huge improvements in how reps are doing better discovery and driving more revenue. The right tool can significantly boost sales productivity. Our team, for instance, has uncovered deeper customer needs during discovery calls, thanks to AI-driven guidance. Predictive analytics has enhanced our visibility into early-stage deals, helping us prioritize and forecast more effectively. But the benefits go further. A well-designed Sales GenAI tool should: • Surface customer sentiment and disposition trends. • Identify recurring objections and competitive mentions. • Provide actionable insights, not just data dumps. If you're exploring GenAI tools to boost sales productivity with real insights and actionable predictions, it's important to look beyond the buzzwords. Consider these five key factors: • Goal Alignment: Not all GenAI tools are created equal. Choose a solution that aligns with your specific goals, whether that's improving discovery calls, forecasting deals or surfacing customer insights. • Customization: One-size-fits-all rarely works. While some vendors offer templates for popular sales methodologies like MEDDIC, these can be too generic. Look for a tool that adapts to your unique sales processes, not one that forces you to adapt to its limitations. • Cost: Customization often comes at a price. Understand the full scope of investment, including professional services and deployment costs investment before committing. • Deeper Insights: Recording calls and generating transcripts is table stakes. The real value lies in the insights. Choose a tool that goes beyond surface-level data to deliver actionable intelligence, not just a searchable archive of conversations. • Collaboration: Successful implementation requires cross-functional collaboration. Engage key stakeholders—SalesOps, sales leadership, enablement, demand gen and marketing—from the start. Their input is critical in defining business requirements, validating the tool's effectiveness and ensuring long-term success. Final Thoughts AI-powered sales productivity tools can transform how your organization sells, but only if they're aligned with your unique needs and goals. There's no universal solution, no magic metric that guarantees success. The real value lies in selecting the right tool and tailoring it to your business. Some GenAI tools deliver measurable impact, others fall short. The difference? It's both science and art. Science is in the strength of the GenAI engine. Art is in how well it's customized to reflect your sales processes, customer journey and organizational priorities. This isn't just a tech decision, it's a strategic one. Take the time to evaluate, involve the right stakeholders and plan for long-term success. With thoughtful implementation, the right GenAI tool can unlock insights, drive efficiency and elevate your sales performance. The journey starts now. Are you ready to lead it? Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?
Forbes
23-05-2025
- Business
- Forbes
True Costs Of Enterprise Firewall Refreshes
Mickey Singh is a senior leader at Versa Networks, renowned for his proven track record in commercializing emerging technologies. Investing in technology is crucial for businesses to protect their networks, data, devices and users from external threats. Depending on which source you look at, most experts suggest a firewall refresh every five to seven years. A five-year refresh cycle is just about right for most organizations. Wait too long, and you might fall behind on the latest cybersecurity threats or miss out on improved performance. Jump in too soon, and you could end up spending more than necessary. The following factors may dictate the need for a refresh of firewalls: • Increased Security Threats: To combat evolving cyber threats, it's essential to stay ahead of cybercriminals who continually find new ways to exploit vulnerabilities. • Demand For Higher Performance: As cloud and SaaS adoption grows, older firewalls may struggle to meet increased throughput demands. • End-Of-Support (EOS): Firewall manufacturers frequently declare EOS dates for their older appliances, indicating that they will cease providing updates, support and replacement parts. • Staying Ahead Of Technology: Existing hardware may not be able to support advanced security functions like advanced threat protection (ATP) or innovative technologies, such as AI. Keeping up with technology isn't cheap. For decades, firewall vendors have relied on constant refresh cycles to maintain their profit pools. Why do vendors want to push for renewals? • Increased Revenue: Every refresh creates a revenue opportunity for a vendor to push for a "like-for-like" upgrade. But if your needs have changed, you need to look at newer architecture. • Transition To Subscription Models: Vendors are motivated to shift to a recurring revenue model. • Bundled Services: A firewall refresh can be used as a means to bundle (or seemingly give away) additional services such as SD-WAN, ATP, IoT security, etc. • Renegotiate Agreements: Refresh cycles open an opportunity to renegotiate license agreements. This can lock in customers for extended periods and increase revenue. Such strategies enable firewall vendors to capitalize on the enterprise need for updated security infrastructure in an ever-evolving security landscape. For example, in its Q1 2025 Earnings Report, Fortinet shared that a quarter of its installed base is facing EOS (end of support) by 2026. This is based on earlier generations of chips (mainly SOC3 and NP6). This will impact the customers who purchased firewalls during the 2021-2022 'super cycle' and follow the typical five-year upgrade cycle. The same is true for other leading firewall vendors. Lately, I've seen a number of discussions from customers online who have reported significant price increases on renewals and expensive forced hardware refreshes. As a result, many enterprise customers are looking to switch vendors due to concerns about total cost of ownership with constant price hikes during refresh cycles. A firewall refresh provides an excellent opportunity to migrate to a modern framework. Zero trust has surfaced as a way to ensure a robust, adaptive security posture. Here are some key reasons why zero trust is important to future-proof your security infrastructure: • Eliminates implicit trust by continuously verifying every user and device, regardless of location. • Reduces attack surfaces by segmenting the network and controlling access strictly. Firewalls play a critical role in this. • Prevents data breaches by ensuring only authenticated and authorized users can access sensitive data and applications. • Simplifies compliance by consistently applying security policies across the enterprise. • Enhances visibility and control of network traffic and user activities and detects and blocks malicious activities. A well-planned firewall refresh goes beyond acquiring new equipment; it's about building a more secure, efficient and scalable security infrastructure. It's an opportunity to reassess the appliance sprawl at your sites and consolidate multiple functions—security, SD-WAN and, if need be, a switch into a single device. It is an opportunity to simplify your architecture and reduce costs, not just today but also for the next decade. Consider extending this consolidation to the LANs, too. Software-driven LAN (SD-LAN) could be an excellent opportunity to combine security, routing, network services and switching to trim costs and improve agility. Careful planning and comparative analysis of your incumbent firewalls with other firewall solutions can enhance your security to keep up with emerging threats, increase performance to support throughput demands and potentially reduce your total cost of ownership. Consider the following when evaluating enterprise firewalls: • Security Effectiveness: How effectively the firewall protects and controls network access, applications and users while preventing threats and blocking malicious traffic. • Threat Prevention: How effective the firewall is in protecting a trusted network from an untrusted network while allowing authorized communications to pass through. • Rated Throughput: How the firewall performed under various adverse conditions such as maximum connections, transactions per second, concurrency, throughput and latency. • Price & Value: How cost-effective the firewall is in relation to performance, manageability and security. • Vendor Lock-In: When software, ASICs (Application Specific Integrated Circuits) and hardware are tightly coupled in firewalls, vendor lock-in is inevitable. To mitigate this, consider using open standards, modular architecture and multi-vendor environments. Instead of relying on vendor-driven refresh cycles, reassess your security and performance needs. Evaluate if a new architecture or vendor can better support zero trust, cloud and SD-WAN priorities cost-effectively. Leverage the refresh cycle as a strategic inflection point. Consolidate firewall, SD-WAN, SASE/SSE, IoT security and even LAN infrastructure. This will reduce operational complexity, lower total cost of ownership and future-proof your network. Before committing to any refresh, review third-party evaluations, such as those from or Gartner. Use objective data on performance and price/performance ratios to evaluate comparatively, negotiate better terms and choose the best-fit solution for your enterprise. In conclusion, to align with modern needs, reassess your security and performance requirements, consolidate security functions to simplify operations and reduce costs and demand independent validation and cost transparency before committing to any refresh. Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?
