The Evolving cybersecurity Landscape: Consolidating Defenses in the Age of AI
The cybersecurity world is undergoing a rapid conversion, driven by the increasing sophistication of AI-powered threats. Traditional security approaches are proving insufficient, demanding a shift towards consolidation and proactive, real-time defenses. This article explores the key changes happening now and what you need to do to secure your institution.The Decline of Standalone CRR Tools
For years, organizations relied on standalone Cyber Risk ratings (CRR) tools to assess third-party vulnerabilities. Tho, thes are becoming less effective. A more integrated approach is now essential. Fortunately, modern Third-Party Risk Management (TPRM) and continuous monitoring platforms are stepping in to fill the gap. Solutions from providers like UpGuard, Panorays, and RiskRecon offer a more holistic and dynamic view of your extended ecosystem. They replace the limitations of isolated CRR assessments.Automation is Key to Rapid Response
Responding to vulnerabilities quickly is paramount. Manual remediation processes simply can’t keep pace with todayS threat landscape. Automated remediation is now a critical capability. microsoft’s Defender Vulnerability management (MDVM) paired with Intune, Tanium‘s endpoint management solutions, and DevOps-focused tools like Mondoo are leading the charge. These platforms enable real-time threat neutralization, significantly reducing your organization’s attack surface.Securing AI at the “Inference Edge”
As you integrate Artificial Intelligence into your operations, a new vulnerability emerges: the “inference edge.” This is where AI models are deployed and actively making decisions.Securing this point is crucial.Consolidating your security tools at this edge is the future of cybersecurity. Failing to do so risks losing control over your AI deployments and exposing your organization to important threats.Here’s what you need to prioritize: Consolidate controls decisively. Streamline your security stack to focus on the most critical defenses at the inference edge. Implement behavioral anomaly detection. Identify and respond to unusual activity that could indicate a compromise. Strengthen Retrieval-Augmented Generation (RAG) systems. Verify the source of details used by RAG models and establish clear “abstain” paths when data is uncertain. Invest in runtime defenses. Protect your AI systems while they are actively operating. * Empower specialized teams. Ensure you have skilled personnel to operate and maintain these advanced defenses. By embracing these strategies, you can achieve secure AI deployments at scale and navigate the evolving cybersecurity landscape with confidence. The time to act is now.“With volatility now the norm, security and risk leaders need practical guidance on managing existing spending and new budgetary necessities,” states Forrester’s 2026 Budget Planning Guide, revealing a fundamental shift in how organizations allocate cybersecurity resources.
Software now commands 40% of cybersecurity spending, exceeding hardware at 15.8%, outsourcing at 15% and surpassing personnel costs at 29% by 11 percentage points while organizations defend against gen AI attacks executing in milliseconds versus a Mean Time to Identify (MTTI) of 181 days according to IBM’s latest Cost of a Data Breach Report.
Three converging threats are flipping cybersecurity on its head: what once protected organizations is now working against them. Generative AI (gen AI) is enabling attackers to craft 10,000 personalized phishing emails per minute using scraped LinkedIn profiles and corporate communications. NIST’s 2030 quantum deadline threatens retroactive decryption of $425 billion in currently protected data. Deepfake fraud that surged 3,000% in 2024 now bypasses biometric authentication in 97% of attempts, forcing security leaders to reimagine defensive architectures fundamentally.
Caption: Software now commands 40% of cybersecurity budgets in 2025, representing an 11 percentage point premium over personnel costs at 29%, as organizations layer security solutions to combat gen AI threats executing in milliseconds. Source: Forrester’s 2026 Budget Planning Guide
The Evolving Cybersecurity Landscape: Consolidating Defenses in the Age of AI
The cybersecurity world is undergoing a rapid transformation, driven by the increasing sophistication of AI-powered threats. traditional security approaches are proving insufficient, demanding a shift towards consolidation and proactive, real-time defenses.This article explores the key changes happening now and what you need to do to secure your organization.The Decline of Standalone CRR Tools
For years, organizations relied on standalone Cyber Risk Ratings (CRR) tools to assess third-party vulnerabilities. However, these are becoming less effective. A more integrated approach is now essential.Fortunately, modern Third-Party Risk Management (TPRM) and continuous monitoring platforms are stepping in to fill the gap. solutions from providers like UpGuard, Panorays, and RiskRecon offer a more holistic and dynamic view of your extended ecosystem. They replace the limitations of isolated CRR assessments.Automation is Key to Rapid Response
Responding to vulnerabilities quickly is paramount.Manual remediation processes simply can’t keep pace with today’s threat landscape. Automated remediation is now a critical capability. Microsoft’s Defender Vulnerability Management (MDVM) paired with Intune, Tanium’s endpoint management solutions, and DevOps-focused tools like Mondoo are leading the charge. These platforms enable real-time threat neutralization, significantly reducing your organization’s attack surface.securing AI at the “Inference Edge”
As you integrate Artificial Intelligence into your operations, a new security challenge emerges: the “inference edge.” This is where AI models are deployed and actively making decisions. It’s also a potential point of vulnerability. Consolidating your security controls at this edge is crucial. Failing to do so risks losing control over your AI deployments and exposing your organization to new threats. Here’s what you need to prioritize: Robust Behavioral Anomaly Detection: Identify and respond to unusual activity that could indicate a compromise. Strengthened Retrieval-Augmented Generation (RAG) Systems: Implement provenance checks to verify the source of information and define “abstain paths” to prevent AI from generating potentially harmful outputs. Heavy Investment in Runtime Defenses: Focus on protecting your systems while they are operating, rather than relying solely on preventative measures. Empower Specialized Teams: Ensure you have skilled personnel dedicated to operating and maintaining these advanced defenses. Ultimately, a proactive and consolidated security strategy is the key to achieving secure AI deployments at scale. By embracing these changes, you can navigate the evolving threat landscape and protect your organization from emerging risks.The Evolving Cybersecurity Landscape: Consolidating Defenses in the Age of AI
The cybersecurity world is undergoing a rapid transformation, driven by the increasing sophistication of AI-powered threats. Traditional security approaches are proving insufficient, demanding a shift towards consolidation and proactive, real-time defenses. This article explores the key changes happening now and what you need to do to secure your organization.The Decline of standalone CRR Tools
For years, organizations relied on standalone Cyber Risk Ratings (CRR) tools to assess third-party vulnerabilities. though, these are becoming less effective. A more integrated approach is now essential. Fortunately, modern Third-Party Risk Management (TPRM) and continuous monitoring platforms are stepping in to fill the gap. Solutions from providers like UpGuard, Panorays, and RiskRecon offer a more holistic and dynamic view of your extended ecosystem. They replace the limitations of isolated CRR assessments.Automation is Key to Rapid Response
Responding to vulnerabilities quickly is paramount.Manual remediation processes simply can’t keep pace with today’s threat landscape. Automated remediation capabilities are now a critical necessity. Several platforms are leading the way: Microsoft Defender Vulnerability Management (MDVM) with Intune: This combination provides powerful vulnerability detection and automated patching across your endpoints. Tanium’s Endpoint Management: Offers comprehensive visibility and control over your entire endpoint surroundings, enabling rapid response to threats. Mondoo: Focuses on devops-centric security, automating security checks throughout the development lifecycle. These tools empower your security team to neutralize threats in real-time, minimizing potential damage.Securing AI at the “Inference Edge”
As you integrate Artificial Intelligence into your operations, a new security challenge emerges: the “inference edge.” this is where AI models are deployed and make decisions. It’s also a potential point of vulnerability. Consolidating your security controls at this edge is crucial. Failing to do so could lead to a loss of control over your AI systems and the data they process. Here’s how to approach this: Consolidate Controls: Streamline your security stack, focusing on robust defenses at the inference edge. Behavioral Anomaly detection: Implement systems that can identify unusual activity, indicating a potential compromise. Strengthen RAG Systems: Enhance Retrieval-Augmented Generation (RAG) systems with provenance checks and clearly defined “abstain” paths to prevent the generation of inaccurate or harmful information. Invest in Runtime defenses: prioritize defenses that operate while AI models are actively running, providing real-time protection. Empower Specialized Teams: Support the teams responsible for operating and maintaining these advanced security systems. Ultimately, a proactive and consolidated security strategy is the key to achieving secure AI deployments at scale. By embracing these changes,you can navigate the evolving threat landscape and protect your organization from emerging risks.The Evolving Cybersecurity Landscape: Consolidating Defenses in the Age of AI
the cybersecurity world is undergoing a rapid transformation, driven by the increasing sophistication of AI-powered threats. Traditional security approaches are proving insufficient, demanding a shift towards consolidation and proactive, real-time defenses. This article explores the key changes happening now and what you need to do to stay ahead.The Decline of Standalone CRR Tools
For years, organizations relied on standalone Cyber Risk Quantification (CRR) tools. However, these are becoming obsolete. A more integrated approach is now essential. Fortunately, modern Third-Party Risk Management (TPRM) and continuous monitoring platforms are stepping in to fill the gap. Solutions from UpGuard, Panorays, and RiskRecon offer a more holistic view of your risk posture, replacing the limitations of isolated CRR systems.Automation is Key to Rapid Response
Responding to vulnerabilities quickly is paramount. Manual remediation is simply too slow in today’s threat environment.Automated remediation capabilities are now a critical component of a strong security strategy.Consider leveraging: Microsoft Defender Vulnerability Management (MDVM) with Intune: This combination provides powerful endpoint protection and automated patching. Tanium’s Endpoint Management: Offers comprehensive visibility and control over your entire endpoint environment. DevOps-focused solutions like Mondoo: Integrates security directly into your development pipeline, identifying and addressing vulnerabilities early on.These tools enable real-time threat neutralization, significantly reducing your exposure window.Securing AI at the “Inference edge”
As you integrate Artificial Intelligence into your operations, a new vulnerability emerges: the “inference edge.” This is where AI models are deployed and make decisions. Securing this point is now crucial. Failing to consolidate security at this edge could lead to a loss of control and increased risk. here’s how to protect your AI deployments: Consolidate Controls: Streamline your security tools and processes at the inference edge for maximum effectiveness. Behavioral anomaly Detection: Implement systems that identify unusual activity, potentially indicating a compromised AI model. Strengthen RAG Systems: Enhance Retrieval-Augmented generation (RAG) systems with provenance checks to verify data sources and define clear “abstain” paths when information is uncertain. Invest in Runtime defenses: Prioritize defenses that operate while AI models are running, providing real-time protection. Empower Specialized Teams: Support the teams responsible for operating and maintaining these advanced security systems. Ultimately, a proactive and consolidated approach to security is no longer optional. It’s a necessity for navigating the evolving threat landscape and ensuring the secure deployment of AI at scale. By embracing these strategies, you can build a resilient security posture and protect your organization from emerging threats.The Evolving Cybersecurity Landscape: Consolidating Defenses in the age of AI
The cybersecurity world is undergoing a rapid transformation, driven by the increasing sophistication of AI-powered threats. Traditional security approaches are proving insufficient,demanding a shift towards consolidation and proactive,real-time defenses. This article explores the key changes happening now and what you need to do to secure your organization.the Decline of Standalone CRR Tools
For years, organizations relied on standalone Cyber Risk Ratings (CRR) tools to assess third-party vulnerabilities. However, these are becoming less effective. A more integrated approach is now essential. Fortunately, modern Third-Party Risk Management (TPRM) and continuous monitoring platforms are stepping in to fill the gap. Solutions from providers like UpGuard, panorays, and RiskRecon offer a more holistic and dynamic view of your extended ecosystem. They replace the limitations of isolated CRR assessments.Automation is Key to Rapid Response
Responding to vulnerabilities quickly is paramount.Manual remediation processes simply can’t keep pace with today’s threat landscape. Automated remediation is now a critical capability. Microsoft’s Defender Vulnerability Management (MDVM) paired with Intune, Tanium’s endpoint management solutions, and DevOps-focused tools like Mondoo are leading the charge. These platforms enable real-time threat neutralization, significantly reducing your organization’s attack surface.Securing AI at the “Inference Edge”
As you integrate Artificial Intelligence into your operations, a new security challenge emerges: the “inference edge.” This is where AI models are deployed and actively making decisions. It’s also a potential point of vulnerability. Consolidating your security controls at this edge is no longer optional – it’s essential. Failing to do so risks losing control over your AI deployments and exposing your organization to significant threats. Here’s what you need to prioritize: Consolidate controls decisively. Streamline your security stack and focus on integrated solutions. Implement robust behavioral anomaly detection. Identify and respond to unusual activity that could indicate a compromise. Strengthen Retrieval-Augmented Generation (RAG) systems. Verify the source of information and establish clear “abstain” paths when data is uncertain. Invest heavily in runtime defenses. Protect your systems while they are actively operating. * Empower specialized teams. Ensure your security professionals have the expertise and resources they need to operate these advanced defenses. Successfully implementing these strategies will allow you to achieve secure AI deployments at scale. The future of cybersecurity depends on proactive consolidation, automation, and a relentless focus on protecting the inference edge. By embracing these changes, you can build a more resilient and secure organization, ready to face the challenges of the evolving threat landscape.Enterprise security teams managing 75 or more tools lose $18 million annually to integration and overhead alone. The average detection time remains 277 days, while attacks execute within milliseconds.
gartner forecasts that interactive application security testing (IAST) tools will lose 80% of market share by 2026. Security Service Edge (SSE) platforms that promised streamlined convergence now add to the complexity they intended to solve. Meanwhile, standalone risk-rating products flood security operations centers with alerts that lack actionable context, leading analysts to spend 67% of their time on false positives, according to IDC’s Security Operations Study.
The operational math doesn’t work.Analysts require 90 seconds to evaluate each alert,but they receive 11,000 alerts daily. Each additional security tool deployed reduces visibility by 12% and increases attacker dwell time by 23 days, as reported in Mandiant’s 2024 M-Trends Report. Complexity itself has become the enterprise’s greatest cybersecurity vulnerability.
Platform vendors have been selling consolidation for years, capitalizing on the chaos and complexity that app and tool sprawl create. As George Kurtz, CEO of CrowdStrike, explained in a recent VentureBeat interview about competing with a platform in today’s mercurially changing market conditions: “The difference between a platform and platformization is execution. You need to deliver immediate value while building toward a unified vision that eliminates complexity.”
CrowdStrike’s Charlotte AI automates alert triage and saves SOC teams over 40 hours every week by classifying millions of detections at 98% accuracy; that equals the output of five seasoned analysts and is fueled by Falcon Complete’s expert-labeled incident corpus.
“We couldn’t have done this without our Falcon Complete team,” Elia zaitsev, CTO at CrowdStrike, told VentureBeat in a recent interview. “They do triage as part of their workflow, manually handling millions of detections. That high-quality, human-annotated dataset is what made over 98% accuracy possible. We recognized that adversaries are increasingly leveraging AI to accelerate attacks. With Charlotte AI, we’re giving defenders an equal footing, amplifying their efficiency and ensuring they can keep pace with attackers in real time.”
CrowdStrike, Microsoft’s Defender XDR with MDVM/Intune, Palo Alto Networks,Netskope, Tanium and Mondoo now bundle XDR, SIEM and auto-remediation,transforming SOCs from delayed forensics sessions to the ability to perform real-time threat neutralization.
Security budgets surge 10% as gen AI attacks outpace human defense
Forrester’s guide finds 55% of global security technology decision-makers expect significant budget increases in the next 12 months. 15% anticipate jumps exceeding 10% while 40% expect increases between 5% and 10%. This spending surge reflects an asymmetric battlefield where attackers deploy gen AI to simultaneously target thousands of employees with personalized campaigns crafted from real-time scraped data.
Attackers are making the most of the advantages they’re getting from adversarial AI, with speed, stealth and highly personalized, target attacks becoming the most lethal. “For years, attackers have been utilizing AI to their advantage,” Mike Riemer, Field CISO at Ivanti, told VentureBeat. “Though, 2025 will mark a turning point as defenders begin to harness the full potential of AI for cybersecurity purposes.”
Caption: 55% of security leaders expect budget increases above 5% in 2026, with Asia Pacific organizations leading at 22% expecting increases above 10% versus just 9% in North America. Source: Forrester’s 2026 Budget Planning Guide
Regional spending disparities reveal threat landscape variations and how CISOs are responding to them. Asia Pacific organizations lead with 22% expecting budget increases above 10% versus just 9% in North America. Cloud security, on-premises technology and security awareness training top investment priorities globally.
Software dominates budgets as runtime defenses become critical in 2026
VentureBeat continues to hear from security leaders about how crucial protecting the inference layer of AI model development is. Many consider it the new frontline of the future of cybersecurity. Inference layers are vulnerable to prompt injection, data exfiltration, or even direct model manipulation.These are all threats that demand millisecond-scale responses, not delayed forensic investigations.
Forrester’s latest CISO spending guide underscores a profound shift in cybersecurity spending priorities, with cloud security leading all spending increases at 12%, closely followed by investments in on-premises security technology at 11%, and security awareness initiatives at 10%. These priorities reflect the urgency CISOs feel to strengthen defenses precisely at the critical moment of AI model inference.
“At Reputation, security is baked into our core architecture and enforced rigorously at runtime,” Carter Rees, Vice President of Artificial Intelligence at Reputation, recently told VentureBeat. “The inference layer, the exact moment an AI model interacts with people, data, or tools, is where we apply our most stringent controls. Every interaction includes authenticated tenant and role contexts, verified in real-time by an AI security gateway.”
Reputation’s multi-tiered approach has become a de facto gold standard, blending proactive and reactive defenses. “Real-time controls promptly take over,” Rees explained. “Our prompt firewall blocks unauthorized or off-topic inputs instantly, restricting tool and data access strictly to user permissions. Behavioral detectors proactively flag anomalies the moment they occur.”
This rigorous runtime security approach extends equally into customer-facing systems.“For natural language interactions, our AI only pulls from explicitly customer-approved sources,” Rees noted. “Each generated response must transparently cite its sources. We verify citations match both tenant and context, routing for human review if they do not.”
Quantum computing’s accelerating risk
Quantum computing is quickly evolving from a theoretical concern into an immediate enterprise threat. Security leaders now face “harvest now, decrypt later” (HNDL) attacks, where adversaries store encrypted data for future quantum-enabled decryption. Widely used encryption methods like 2048-bit RSA risk compromise once quantum processors reach operational scale with tens of thousands of reliable qubits.
The National Institute of Standards and Technology (NIST) finalized three critical Post-Quantum Cryptography (PQC) standards in August 2024, mandating encryption algorithm retirement by 2030 and full prohibition by 2035. Global agencies, including Australia’s Signals Directorate, require PQC implementation by 2030.
Forrester urges organizations to prioritize PQC adoption for protecting sensitive data at rest, in transit, and in use. Security leaders should leverage cryptographic inventory and discovery tools, partnering with cryptoagility providers such as Entrust, IBM, Keyfactor, palo Alto Networks, QuSecure, SandboxAQ, and Thales.Given quantum’s rapid progression, CISOs need to factor in how they’ll update encryption strategies to avoid obsolescence and vulnerability.
Explosion of identities is fueling an AI-driven credential crisis
Machine identities now outnumber human users by a staggering 45:1 ratio, fueling a credential crisis beyond human management.Forrester’s guide underscores scaling machine identity management as mission-critical to mitigating emerging threats. Gartner forecasts identity security spending to nearly double, reaching $47.1 billion by 2028.
Traditional endpoint approaches aren’t capable of slowing down a growing onslaught of adversarial AI attacks. Ivanti’s Daren Goeson recently told VentureBeat: “As these endpoints multiply, so does their vulnerability. Combining AI with Unified Endpoint Management (UEM) is increasingly essential.” Ivanti’s AI-driven Vulnerability risk Rating (VRR) illustrates this benefit, enabling organizations to patch vulnerabilities 85% faster by identifying threats traditional scoring methods overlook, making AI-driven credential intelligence enterprise security at scale.
“Endpoint devices such as laptops, desktops, smartphones, and IoT devices are essential to modern business operations. However,as their numbers grow,so do the opportunities for attackers to exploit endpoints and their applications,”Goeson explained. “Factors like an expanded attack surface, insufficient security resources, unpatched vulnerabilities, and outdated software contribute to this rising risk. By adopting a comprehensive approach that combines UEM solutions with AI-powered tools,businesses significantly reduce their cyber risk and the impact of attacks,” Goeson advised VentureBeat during a recent interview.
Forrester saves their immediate call to action in the guide for advising security leaders to begin divesting legacy security tools immediately,with a specific focus on interactive application security testing (IAST),standalone cybersecurity risk-rating (CRR) products,and fragmented Security Service Edge (SSE),SD-WAN,and Zero Trust Network Access (ZTNA) solutions.
Rather, Forrester advises, security leaders need to prioritize more integrated platforms that enhance visibility and streamline management. unified Secure Access Service Edge (SASE) solutions from Palo Alto Networks and netskope now provide essential consolidation.At the same time, integrated Third-Party Risk Management (TPRM) and continuous monitoring platforms from UpGuard,Panorays and RiskRecon replace standalone CRR tools the consulting firm advises.
Additionally, automated remediation powered by Microsoft’s MDVM with Intune, Tanium’s endpoint management, and DevOps-focused solutions like Mondoo has emerged as a critical capability for real-time threat neutralization.
CISOs must consolidate security at AI’s inference edge or risk losing control
Consolidating tools at inference’s edge is the future of cybersecurity, especially as AI threats intensify. “For CISOs, the playbook is crystal clear,” Rees concluded.“Consolidate controls decisively at the inference edge. Introduce robust behavioral anomaly detection. Strengthen Retrieval-Augmented Generation (RAG) systems with provenance checks and defined abstain paths.Above all,invest heavily in runtime defenses and support the specialized teams who operate them. Execute this playbook, and you achieve secure AI deployments at true scale.”
