For National Insider Threat Awareness Month 2025, the editors at Solutions Review have compiled a list of comments from some of the leading industry experts.

As part of this year’s National Insider Threat Awareness Month, we called for the industry’s best and brightest in Identity and Access Management and the broader cybersecurity market to share best practices, predictions, and personal anecdotes. The experts featured represent some of the top influencers, consultants, and solution providers with experience in these marketplaces, and each quote has been vetted for relevance and ability to add business value.

National Insider Threat Awareness Month Quotes from Industry Experts in 2025

Jake Bell, Engineer Team Lead at Object First

“This National Insider Threat Awareness month is a reminder that one of the biggest risks to an organization’s data often can come from within the company. Whether through malicious intent or simple human error, insiders can inadvertently open the door to catastrophic breaches. The most dangerous mindset for any organization is believing ‘it won’t happen to us.’ In today’s threat landscape, leaders must operate under the assumption that a breach is inevitable. This means that secure, tested, and adaptable backup strategies are a non-negotiable.

“This month, IT teams should ensure Zero Trust Disaster Resilience (ZTDR) practices are incorporated into their storage infrastructure. With ZTDR, admins can truly harden their data protection architectures by segmenting backup software and storage, creating resilience zones, and leveraging immutable backups as the final line of defense when attackers slip past defenses. Whether through shadow IT, evolving AI tools, or the click of an unsuspecting employee, with immutable backups and ZTDR principles in place, organizations ensure recovery remains possible even in worst-case insider threat scenarios. Awareness is important, but resilience is essential.”

Patrick Harding, Chief Architect at Ping Identity

“Insider threats have long been a security risk for organizations, but the attack surface is expanding into new territory: AI agents can now act like internal users with their own access and behavior patterns. With 79 percent of senior executives reporting that AI agents are already being adopted in their companies, we’re facing a very stark reality where determining human behavior from bot behavior might be the difference between securing your organization and falling victim to a nefarious attacker.

“Whether it’s a malicious insider, a negligent employee, or an ungoverned AI agent behaving unexpectedly, the fallout from insider threats can be disastrous and long-lasting. That’s why early detection, including identifying unusual patterns like unexpected login attempts or unusual data access, is critical. However, detection alone isn’t enough. Real‑time risk assessment is essential to immediately identify and prioritize the most urgent threats. Finally, decisive actions, including escalating to security operations or enforcing stricter policies, must follow. This month is an important reminder to recognize that every identity, human or AI, needs to be treated with the same level of caution and verification.”

Pete Luban, Field CISO at AttackIQ

“Insider threats, whether from disgruntled employees or compromised credentials, are challenging to detect and prevent with traditional security measures. Insider Awareness Month serves as a reminder to security teams about the importance of simulating real-world insider attack scenarios to assess the effectiveness of their security controls and response protocols.

“Recent spikes in shadow AI usage and a lack of proper cyber hygiene increase the likelihood of insider threats. Using unauthorized tools or platforms can unknowingly expose sensitive data or create exploitable vulnerabilities, as well as poor security practices, like maintaining outdated software or weak passwords.

“By integrating techniques, such as adversarial emulation, into the security lifecycle, organizations can uncover gaps in their detection and mitigation strategies before a real attack occurs. Simulated, continuous testing can ensure that security teams can mitigate attacks before insider threats sidestep defenses and steal valuable company data.”

Joshua Roback, Principal Security Solution Architect at Swimlane

“Insider threats have always been one of the hardest challenges for security teams because they originate from people with legitimate access. Unlike external adversaries, they don’t have to find a way in. They already have the keys. That makes their actions harder to spot and far more damaging when they turn malicious or careless.

“It’s up to organizations to ensure their security systems are well-protected, starting with determining who has access to which systems. Poorly managed access controls can create an environment for insider threats to sprout and thrive. Implementing a mature identity access management solution is the most powerful weapon in mitigating insider threat risks. User behavioural analytics (UBA) can provide proactive detection of anomalous user behaviors, giving security teams a leg up against unannounced attackers.

“The rise of insider threats has resulted in the development of security measures that can ensure that threats are monitored, analyzed, and neutralized before they escalate into catastrophic breaches. Building resilience has required organizations to combine continuous monitoring, automated response, and a strong security culture to reduce the window of opportunity for insider abuse.”

Bojan Simic, Co-Founder and CEO at HYPR

September marks Insider Threat Awareness Month, and this year’s theme, ‘Partnering for Progress,’ cuts to the core of what’s failing in enterprise security today.

“Insider threat mitigation is not an isolated security problem. It’s a company-wide imperative—and it starts with identity. Whether posed by a malicious actor or an employee simply making a user error, insider threats consistently compromise organizations by exploiting gaps in how users are verified at sign-in and throughout a session.

“Identity is rightly regarded as the new perimeter, yet it remains one of the most vulnerable points of access. This is because static credentials, one-time authentication, and siloed access controls leave too much room for misuse. Most systems validate an identity once and then blindly assume that risk has been mitigated. It hasn’t.

“But technology is only half the battle. True ‘Partnering for Progress’ means aligning IT, security, HR, and compliance. Without identity assurance embedded across these functions, insider threats will inevitably slip through. The companies that are preventing breaches are not just reacting to threats; they are proactively integrating identity into every strategic decision. They are the ones who are building a resilient, security-first culture from the inside out.”

Aditya Sood, VP of Security Engineering and AI Strategy at Aryaka

“Insider Threat Awareness Month is a critical initiative for raising awareness about the unique security risks posed by internal actors. There have been several examples of insider threats wreaking havoc on major corporations, with Elon Musk’s X being the most prominent recent example.

“A malicious insider is a significant cybersecurity risk, as such individuals can steal intellectual property, exfiltrate confidential information, sabotage systems, or manipulate business operations for personal gain or in collusion with outside threats. The impact can range from financial losses and reputational damage to regulatory penalties and national security risks.

“Awareness about malicious insider activities is crucial because employees and stakeholders must understand the importance of safeguarding credentials and the necessity of reporting suspicious activity. By teaching employees to recognize the signs of suspicious behavior and reinforcing the importance of strict access controls and reporting protocols, organizations can transform their entire workforce into a crucial line of defense against internal threats. Employees’ role in this is not just important: it’s indispensable. They are the first line of defense, and their commitment to this cause will keep organizations secure.”

Steve Wilson, Chief AI and Product Officer at Exabeam

“The danger from insider threats continues to grow in the modern cyber landscape, particularly as AI accelerates their speed, stealth, and sophistication. With 64 percent of cybersecurity professionals now viewing insiders as a greater risk than external actors, Insider Threat Awareness Month is a critical opportunity to emphasize proactive defense strategies.

“While 88 percent of organizations have insider threat programs, many lack behavioral analytics needed to detect AI-enhanced attacks that exploit trusted access and mimic legitimate user behavior. As threats intensify across sectors like government, healthcare, and manufacturing, this initiative provides an opportunity to call for stronger governance, cross-functional collaboration, and real-time detection capabilities to stay ahead of both human and AI-driven insider risks.”

Mark Wojtasiak, VP of Product Research and Strategy at Vectra AI

“Insider Threat Awareness Month is a reminder that the challenge isn’t just at the perimeter; it’s inside organizations, where identities, networks, and everyday user behavior are constantly at play. Security teams are inundated with thousands of alerts daily, yet only a small fraction represent real threats. This noise leaves many analysts unable to review more than a third of alerts, and the fear of missing an attack is a weekly reality for most SOC professionals. Ultimately, this noise drowns out the signal that matters most—the activity rooted in how identities are used and how they traverse the network.

“Compounding this, recent industry research shows that insider threats, particularly non-privileged users whose accounts are compromised or misused, are now the most prevalent attacker profile. Nearly two out of five prioritized threats are tied to insider behaviors. The reality is that user and identity misuse is inevitable in today’s complex networks and environments. That’s why security leaders need to focus on detection and response strategies that look beyond the perimeter and zero in on how accounts, identities, networks, and data are actually being used. Reducing noise while elevating the signal that matters most is critical to empowering SOC teams to catch what could otherwise slip through the cracks.”

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Vicky Bruce, Global Capability Manager of Cybersecurity Services at Rockwell Automation, explains why AI can be a double-edged sword for OT/ICS cybersecurity. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Artificial intelligence (AI) is quickly transforming how industrial organizations think about cybersecurity. On one hand, it helps security teams spot threats earlier, automate responses, and reduce downtime. On the other hand, it gives cyber attackers tools to launch more targeted, convincing, and damaging attacks—often in seconds.

Cybersecurity threats are evolving as fast as the technologies meant to stop them. For cybersecurity teams tasked with protecting operational technology (OT) and industrial control systems (ICS), this is both a leap forward and a growing risk. In the field, the same AI model that helps prevent downtime one day can trigger a false positive —or worse, be manipulated—on another. Security teams face the challenge of tapping into AI’s potential without introducing new vulnerabilities.

The Expanding Cyber Risk Landscape 

Industrial networks today look nothing like they did a decade ago. What were isolated, largely air-gapped, industrial networks are now interconnected ecosystems where OT and information technology (IT) converge. Meanwhile, cyber threats are growing in scale and complexity, and the convergence of IT and OT is increasing the attack surface. According to the SANS 2024 ICS/OT Cybersecurity Report, the cybersecurity risks in OT are growing, with 19 percent of organizations reporting one or more security incidents in just a year.

AI is accelerating progress on both sides of the cybersecurity equation. A recent survey of manufacturing leaders found that 49 percent plan to use AI and machine learning (ML) for cybersecurity in the next 12 months. But the same tools are also being used by threat actors to automate intrusions and evade detection. The challenge is to harness AI’s potential while keeping it from being weaponized against the systems it was designed to protect.

New Frontiers in Protecting Operational Technology

AI’s strength lies in its ability to process and act on vast amounts of data. When applied to industrial environments, it can recognize subtle changes before they become major disruptions or threats. Here’s where it’s making a difference:

Smarter Anomaly Detection  

Traditional threat detection tools look for known signatures, but many of today’s most damaging threats don’t come with a fingerprint. AI-driven threat detection systems can flag subtle behavioral anomalies, such as a robotic arm cycling 0.4 seconds too fast or a PLC issuing a command slightly out of sequence. Even an unusual pattern in equipment startup time can signal misconfiguration caused by a compromised vendor laptop.

Predictive Maintenance as a Security Layer  

AI-powered predictive maintenance can serve as an added layer of a strong cybersecurity strategy. A piece of equipment acting “off schedule” could be more than just wear and tear. It might be a symptom of malware or unauthorized configuration changes. Continuously monitoring maintenance data to flag irregularities can help teams identify potential failures before they happen.

AI-Assisted Network Segmentation  

When a breach happens, the difference between a minor incident and a catastrophic shutdown comes down to speed. Seconds can determine whether a threat jumps to another cell or stays isolated. In a food and beverage plant, this could mean stopping a ransomware attack before it locks down a batching system. Instead of waiting for IT teams to intervene manually, AI confirms threats are contained in real-time.

When Cyber Defenses Become a Target

Of course, the same technology deployed to defend operations can be weaponized. Attackers are using AI to design malware that adapts, evades, and even changes itself, rendering traditional security technology relying on fixed threat databases increasingly less useful. At the same time, AI-generated deepfakes make phishing attempts more realistic than ever. Take a manager on the plant floor receiving a voicemail from their “CEO” authorizing a key system modification, then later learning that it was entirely AI-generated.

Attackers are also testing how far they can manipulate AI systems directly. By feeding adversarial data into detection models, they can suppress alerts or train systems to ignore certain behaviors. Without proper validation, a security model might learn the wrong lessons from the wrong data.

Recent high-profile ransomware incidents reinforce how quickly tactics are evolving. For example, a ransomware attack disrupted operations for thousands of U.S. car dealerships and led to a reported $25 million ransom payment. This example demonstrates how threat actors are employing more advanced tactics to cripple businesses and shut down entire industries. These are no longer isolated events, but industry-shaping moments.

Best Practices for AI in OT Cybersecurity 

To safely deploy AI in critical infrastructure, organizations need more than just good intentions. They need good governance. This includes:

• Implementing security frameworks. AI-driven security measures should follow industry best practices. By aligning with established frameworks like NIST 800-82 and IEC 62443, organizations can take a structured approach to safeguarding operational technology environments in the face of growing OT/IT convergence challenges.
• Testing early and often. Without rigorous testing and validation, AI models can be tricked into ignoring or misclassifying real threats. Regular testing helps detect vulnerabilities and prevent adversarial manipulation. Organizations can also use AI to simulate intrusions, running AI-driven penetration tests to identify weaknesses before malicious actors can exploit them.
• Embedding security from the start. Additionally, AI should be deployed using a “secure-by-design” approach, where security is embedded into AI systems from the outset rather than treated as an afterthought. The future of AI in cybersecurity isn’t just about a stronger posture—it’s about staying ahead of threat actors who are using the same methods.

Balancing AI Innovation and Risk

As OT/IT convergence continues to blur the lines between traditional IT networks and industrial systems, AI is reshaping industrial cybersecurity. However, it’s a double-edged sword. Used correctly, it can enhance threat detection, automate risk management, and keep OT environments safer than ever. If left unchecked, though, it can introduce new vulnerabilities and give threat actors even more powerful tools. Security leaders must keep their eyes open to this tension to confirm their organizations benefit from AI’s capabilities without becoming over-reliant or exposed to new forms of risk.

The secret is balance. Used wisely, AI is a strategic advantage. Industrial organizations can strengthen security by implementing AI responsibly, validating models, and staying ahead of emerging threats without sacrificing resilience. In today’s high-stakes cybersecurity landscape, that’s the kind of AI strategy that wins.

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Prasobh Veluthakkal, Focaloid Technologies‘ CTO, provides his perspective on the next generation of threat intelligence and AI-powered cyber threats. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

In 2025, the cybersecurity landscape will have taken on an entirely different level of sophistication: AI and machine learning will be the future attack vectors, and teams will need AI to master them. As a technologist who experienced the transition from signature-based detection to behavioral analytics, I see a significant shift in how attackers work, and our enterprise counterparts had better take notice. Recent industry intelligence shows a 19 percent climb in CISO concerns around AI-powered cyber threats, indicating that conventional security paradigms desperately need a strategic overhaul.

The Maturation of AI-Driven Attack Methodologies

The danger has progressed from traditional strike models into a more complex network of strategic, adaptive campaigns that use machine learning to seek the optimal target. Research shows that 62 percent of CISOs are now more worried about the social engineering threat vector—fuelled by AI—than they were in the past. This move presents a significant departure from the more conventional threat and highlights the impact of the threat being shifted away from a traditional perimeter focus.

Modern adversaries use generative AI to model massive datasets such as social media activity, communication patterns, or organizational structure and create attack vectors tailored to the victim from this information. However, these attacks work at scale, not at the level of one individual, which poses a serious threat to the conventional security awareness programs and human detection. The fact that sophisticated AI tools are becoming increasingly democratized, as is evident by the provision of advanced AI plugins for Cybercrime-as-a-Service platforms, has lowered technical barriers, allowing actors with basic AI knowledge to wage high-level attacks such as those carried out using FraudGPT and WormGPT.

Worst of all, the development of adaptive malware that uses machine learning to adjust behavior in real-time. By monitoring the efficacy of the defenders’ reactions, they tweak how they attack and operate at certain times when capture is ineffective. Conventional signature-based detectors are insufficient to detect polymorphic malware, which changes its code signature continuously to escape static analysis.

Intelligence-Driven Defense Architecture

Today’s increasing threats are not addressed using reactionary campaigns but result from proactive drivers likely to appear in new, disruptive, and strategic cyber events. Successful, resilient organizations have implemented continuous behavior monitoring that establishes a set of baseline patterns of network, application, and user activity. They use machine learning algorithms to analyze minor variations that could indicate a breach, such as spikes in entropy or irregular communication that portend the arrival of advanced persistent threat attacks.

Nowadays, threat intelligence platforms can ingest unstructured data from different sources, including dark web forums, social media channels, and global threat feeds, and correlate these separate indicators of compromise. With natural language processing power, security professionals will digest operational-level data from a sea of data about early warning for attack campaigns before they reach critical infrastructures.

The more sophisticated environments complement automatic responses with human decision-making, to ensure tactical decisions taken under the pressures of an attack are still in harmony with broader strategic perspectives. The ones that are the most aggressive in applying AI with cybersecurity operations are banking about $2.2 million, partly thanks to quicker response time than what they are used to without AI and better threat detection, on average.

Regulatory Compliance in the AI Era

The regulatory landscape has evolved significantly, with frameworks like CERT-In’s 2025 guidelines mandating comprehensive Bills of Materials (BOMs) that extend beyond traditional software components to include cryptographic elements, AI models, and hardware dependencies. These requirements demand visibility into entire technology stacks, including third-party integrations and cloud service dependencies.

Organizations must now implement continuous audit readiness, maintaining real-time asset inventories that include AI model provenance, training data sources, and algorithmic decision pathways. The six-hour breach reporting requirement has created operational pressure for automated detection and response capabilities, as manual processes cannot meet these stringent timelines.

Strategic Imperatives for Technology Leadership

From our vast experience deploying enterprise security architectures, three key success elements stand out for organizations preparing for the next wave of AI-powered cyber threats. First, you need a unified threat intelligence platform that combines behavioral analytics with global threat data to provide predictive instead of reactive security trends. Such systems must be capable of automated correlation but keep humans in the loop for complex decision-making.

The second is that zero-trust architecture tenants should be enforced—you should assume compromise and verify all transactions regardless of their origin. This model is crucial for combating AI-driven attacks that impersonate human activity and take advantage of perimeter-based security models. Companies must architect a system where access requests are always authenticated and authorized based on real-time risk evaluations.

Third, organizational capabilities for balancing AI adoption with managing risks must be established following an overall governance framework. Technology risk management leaders must ensure AI deployments improve security posture while complying with regulations and maintaining operational resilience. This demands a partnership between security, compliance, and business functions to derive the proper guardrails around the deployment and management of AI systems.

Conclusion

The collision of AI and cybersecurity is the most significant challenge and the biggest opportunity for enterprise technology leadership. Success is only possible with a threat-informed defensive and offensive strategy, including enterprise solutions and an organization committed to constant evolution. Companies that strike this balance will enjoy serious competitive advantages in the digital business world.

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Russell Fishman, the Global Head of Solutions Product Management for AI, Virtualization, and Modern Workloads at NetApp, discusses the security challenges AI can introduce to modern companies. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

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Josh Ray, CEO of Blackwire Labs, recently participated in a panel discussion on Fortress AI and highlighted some of the most relevant takeaways. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

I had the privilege of joining an outstanding group of cybersecurity leaders for Solutions Review’s Insight Jam panel on “Fortress AI: Deploying AI Agents for Ironclad Security.” The conversation with Michael Morgenstern  (DayBlink Consulting), Daniele Catteddu (Cloud Security Alliance), Greg Sullivan (CIOSO Global), Keatron Evans (InfoSec), and Simon Jonker (CSIS Security Group A/S) provided invaluable insights into where AI agents are transforming cybersecurity operations today—and where we’re headed tomorrow.

The Human-AI Partnership: Trust Through Governance

One of the most compelling themes that emerged from the “Fortress AI” panel was the critical balance between AI autonomy and human oversight. As Daniele Catteddu noted, “I see [AI agents] as a tool, though, a tool that is going to augment humans. I doubt that will ever replace them or us, in the sense that there are way too many complexities in order for them to be ungoverned.”

This perspective aligns perfectly with Blackwire Labs’ philosophy. We’ve designed our platform around the principle of human-augmented capability, where AI agents enhance human decision-making rather than replacing it. Our Cybersecurity Community of Excellence (CCOE) of 50+ seasoned professionals sets the foundation for validating and curating the knowledge (to include their wisdom) that powers our AI agents, ensuring that expert human insight remains at the core of every recommendation.

Simon Jonker reinforced this notion by emphasizing that “fundamentally, security boils down to trust. And the best way to build trust is also to do that through relationships… ultimately person to person.”

This is precisely why our TrustWire technology provides blockchain-based verification and complete auditability—trust in AI systems must be built on transparency and verifiable expertise.

The Reality Check: We’re Already Behind

Greg Sullivan delivered a sobering reality check that resonated throughout the discussion: “No cyber-attack I’ve seen in the last few years has been the act of a single team, so we better put ourselves in a position to have at least the tools they’re using against them. And AI is in that category today.”

This observation underscores why Blackwire Labs exists. cyber-criminals have been “professionalizing” their operations for years now, and espionage actors are determined, well-resourced, and continuously increasing in capability, now layering on the ability to leverage AI tools to scale and accelerate their attacks. Meanwhile, many organizations struggle with basic security fundamentals due to resource constraints and skills gaps. Our vendor-agnostic approach democratizes access to enterprise-grade cybersecurity intelligence, helping organizations of all sizes compete against well-resourced adversaries.

Practical AI Applications: Beyond the Hype

The panel showcased several compelling use cases that demonstrate AI’s immediate value in cybersecurity operations:

Vulnerability Management & Threat Mitigation

Michael Morgenstern set the stage perfectly: “The goal seems to be the same: to focus humans on the most critical IQ-intensive functions and automate away all the rest.”

Simon Jonker highlighted a critical use case: “The ability to present, aggregate, and somehow collect all of the information, especially in the network segment… funnel it down into an AI agent that can help you strategize and help you target as an analyst, what are the threats that we are detecting here.”

This mirrors our approach with Blackwire 2.0’s advanced agentic architecture, which intelligently orchestrates multiple data sources and specialized tools to provide contextualized threat intelligence tailored to each organization’s specific environment.

Skills Assessment and Training

Keatron Evans shared an innovative approach to validating cybersecurity skills using AI, noting that many professionals claiming extensive experience actually “assess at the level of someone who’s been doing it for like six months.”

This highlights the skills gap that Blackwire.ai helps bridge by providing expert-level guidance to practitioners at all skill levels and organizational roles.

A Personal Use Case: Turning Compliance into Competitive Advantage

During the panel, I shared a real-world example that illustrates the transformative potential of AI in cybersecurity. Working with a CISO in a large manufacturing OT environment.  The CISO wanted to create a data-driven board presentation to ask for investment to mature portions of his security program. His pragmatic and creative idea was to demonstrate how this investment could not only help drive down organizational risk but also demonstrate how this investment would be offset by potentially reduced cyber insurance premiums; however, he needed data to support his business case.

We used Blackwire.ai to analyze their CIS Controls, focusing on those with a low maturity score. The objective was to first provide specific recommendations and implementation steps to increase the maturity of those controls. Then, assuming those controls had improved, we cross-referenced those results with cyber insurance requirements.

The result? Within minutes, we created an intersection of the specific CIS controls that would simultaneously improve their security posture and (once improved), would significantly reduce their insurance premiums. This targeted and data-driven approach on what to focus on, why, and how to achieve it saved the organization an estimated $160,000-$200,000 in consulting fees. It provided a compelling business case for the board and a prescriptive roadmap to success.

As I noted in the discussion, “That’s an interesting use case that was maybe not something that I actually thought of initially, but in working with our client, actually drove a really positive outcome.” This exemplifies how AI agents can surface unexpected value by connecting disparate data points in ways that humans might miss or more likely not have the time or money to implement.

The Accountability Challenge

Keatron Evans raised a crucial point about AI accountability: “When they adopt AI into their security workflows, that accountability and responsibility matrix is mapped out… the ownership of a decision that was made is somewhere buried into some AI algorithm somewhere, and nobody knows how it got made.”

This is exactly why we took a human-and-technology approach to address this accountability challenge. We’ve invested significant time and resources in creating rigorous source evaluation criteria grounded in intelligence analytical tradecraft. Our Cybersecurity Community of Excellence provides the curated expert knowledge base that forms the foundation of every recommendation. All outputs are completely referenceable, with sources secured via Trustwire technology, so you know the source is authentic and hasn’t been altered. Organizations can trace every recommendation back to its expert-validated sources and demonstrate the integrity of that information to auditors, boards, and regulators.

Looking Ahead: The Evolution of Fortress AI

The panel reinforced my conviction that we’re at an inflection point in cybersecurity. As Keatron Evans wisely advised: “Focus on the problem that you want to solve and then find a way to use AI to solve it—versus just focusing on putting AI into something.”

This problem-first approach is driving our development of Blackwire 2.0, coming out later this year. We’re not adding AI features for the sake of having AI—we’re solving real client problems:

• Workflow automation that eliminates complexity – No need to be an expert prompt engineer or wondering what to ask. The platform adapts to your security processes with intelligent onboarding that captures your organization’s context and configures custom AI personas for each role based on your technology ecosystem and business priorities

• Streamlined intelligence operations – Transform threat reports into automated analysis and strategic deliverables within hours, while turning time-intensive compliance processes into efficient analyses across HIPAA, CMMC, ISO, and NIST frameworks

• Precision-targeted security management – Custom dashboards and daily briefings correlate global threat intelligence with your specific organizational vulnerabilities and business drivers, while automated tabletop exercise workflows generate comprehensive, realistic threat scenarios in hours rather than weeks, dramatically reducing costs while maintaining enterprise-level quality and regulatory compliance requirements

The Path Forward: Measured Adoption with Bold Vision

Daniele Catteddu provided perhaps the most balanced perspective on AI adoption: “I would certainly encourage all of you and everyone in the audience to… go for that. So perhaps if I can make a kind of call for action, I would certainly encourage all of you…to help us in developing those best practices, those frameworks that are going to be able to help us in better governing a technology that… is absolutely fantastic.”

The cybersecurity community must work together to establish governance frameworks for AI adoption. At Blackwire Labs, we’re committed to this collaborative approach and expanding our partnerships with organizations like the Cloud Security Alliance.

Conclusion: Building Trust in an AI-Driven Future

As we deploy AI agents across our security operations, we must remember that the goal isn’t to eliminate human judgment—it’s to augment human capability at scale. Like the “Fortress AI” panel discussion covered, the most successful organizations will be those that can harness AI’s speed and precision while maintaining human oversight and accountability.

That’s the vision we’re building toward at Blackwire Labs, and it’s the conversation we must continue having as an industry.

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Suresh Katukam, the Chief Product Officer and Co-Founder at Nile, explains how purpose-built networking and AI make zero trust security possible. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Ransomware attacks in the U.S. have surged 149 percent year over year. The sheer scale and sophistication of these threats—often powered by AI—are overwhelming traditional security defenses. At the same time, remote work, cloud adoption, and the proliferation of IoT devices have pushed the modern enterprise far beyond the limits of perimeter-based security.

To play catch-up, organizations have implemented many bolted-on controls and quick fixes. This is not working, as Gartner has evidenced. In their 2024 State of Zero Trust Adoption Survey, 63 percent of respondents had either attempted or partially attempted a zero-trust initiative; however, 35 percent reported failures in their initiatives that adversely affected their organization. A fundamentally different approach is needed: one that combines Zero Trust principles with purpose-built network infrastructure and AI-driven automation.

Zero Trust: More Than a Marketing Term

As applications, users, and devices have moved outside the traditional network perimeter, assumptions that led to legacy security models have collapsed. To counter this, the Zero Trust security framework was introduced in 2010 and is based on the simple principle of “never trust, always verify”. Over 15 years later, it’s now more relevant than ever.

Zero Trust is intended to outline the steps needed to enforce least-privilege access, continuously verify identity, and lay the groundwork for segmenting networks that limit lateral movement. It’s a powerful model in theory, but in practice, most organizations struggle to implement it effectively at scale.

Why Zero Trust Initiatives Fail

In Gartner’s more recent “Predicts 2025: Scaling Zero-Trust Technology and Resilience” report, they paint a sobering picture: by 2028, 30 percent of organizations are expected to abandon their zero-trust initiatives, citing complexity, lack of integration, cultural resistance, and limited vendor value. Unless we fundamentally rethink how Zero Trust is implemented, that prediction will likely prove accurate.

Additional barriers include:

• Legacy infrastructure that was never designed for dynamic access control or micro-segmentation.
• Agent-based models like ZTNA that do not work for unmanaged IoT or operational tech devices.
• Operational missteps—everything from misconfigurations to policy sprawl.
• Skill gaps, especially in lean IT teams.

In essence, Zero Trust can’t be reduced to a product. It’s an architectural shift, and unfortunately, many organizations are trying to retrofit Zero Trust principles into environments that were never meant to support them.

Where AI Fits—And Where It Doesn’t

AI plays a critical role in making Zero Trust scalable. But AI alone isn’t enough. If the underlying network is based on legacy principles and vulnerabilities, it is inconsistent, reactive, or fragmented in how Zero Trust is delivered, AI becomes just another bolted-on solution—or worse, a band-aid. This is why a combination of a deterministic network architecture and closed-loop AI automation is so powerful.

A fundamentally different approach must be explored. One where every port and access point is secured by default, with identity-based access baked into the infrastructure, where VLANs, the spanning tree protocol, bolt-on NAC solutions, and a reliance on agents are no longer needed. The network must be designed to enforce consistent access policies across users and devices from day one, regardless of whether they are connecting on campus or remotely.

AI then amplifies this by:

• Monitoring user and entity behavior in real-time across the entire fabric.
• Detecting anomalies and surfacing root causes proactively.
• Reducing the need for manual intervention and guesswork.
• Continuously optimizing policy adherence.

AI and automation should not be bolted on to fix legacy vulnerabilities. Instead, networks should be designed so that security is an outcome of control and visibility, and AI reliably scales that outcome. The better path is to adopt a network designed from the ground up to isolate devices, enforce identity, and deliver policy-based access consistently, without depending on manual configuration or human enforcement.

With AI, this network becomes inherently more secure and intelligent, capable of adapting in real-time as users, devices, and threats evolve.

Again, Start with the Right Foundation

Before launching into a Zero Trust initiative or trying to fix an existing implementation, organizations should ask:

• What are the vulnerabilities in our current network architecture?
• Can our environment support identity-based access and segmentation without complexity?
• How are we scaling security with the resources we have—and where can AI and automation help?
• Are we moving to or enabling Zero Trust, which was built in by design, or are we trying to duct-tape it onto a legacy foundation?

The future of enterprise security isn’t just about AI or Zero Trust in isolation. It’s about unifying both through a purpose-built network architecture, made intelligent by AI. This allows you and your organization to move from aspiration to assurance—and from reactive security to real protection.

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