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March 7, 2025

Darktrace's Early Detection of the Latest Ivanti Exploits

In January 2025, Ivanti disclosed two critical vulnerabilities affecting their products. Darktrace detected exploitation of these vulnerabilities as early as December 2024.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Hugh Turnbull
Cyber Analyst
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07
Mar 2025

As reported in Darktrace’s 2024 Annual Threat Report, the exploitation of Common Vulnerabilities and Exposures (CVEs) in edge infrastructure has consistently been a significant concern across the threat landscape, with internet-facing assets remaining highly attractive to various threat actors.

Back in January 2024, the Darktrace Threat Research team investigated a surge of malicious activity from zero-day vulnerabilities such as those at the time on Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances. These vulnerabilities were disclosed by Ivanti in January 2024 as CVE-2023-46805 (Authentication bypass vulnerability) and CVE-2024-21887 (Command injection vulnerability), where these two together allowed for unauthenticated, remote code execution (RCE) on vulnerable Ivanti systems.

What are the latest vulnerabilities in Ivanti products?

In early January 2025, two new vulnerabilities were disclosed in Ivanti CS and PS, as well as their Zero Trust Access (ZTA) gateway products.

  • CVE-2025-0282: A stack-based buffer overflow vulnerability. Successful exploitation could lead to unauthenticated remote code execution, allowing attackers to execute arbitrary code on the affected system [1]
  • CVE-2025-0283: When combined with CVE-2025-0282, this vulnerability could allow a local authenticated attacker to escalate privileges, gaining higher-level access on the affected system [1]

Ivanti also released a statement noting they are currently not aware of any exploitation of CVE-2025-0283 at the time of disclosure [1].

Darktrace coverage of Ivanti

The Darktrace Threat Research team investigated the new Ivanti vulnerabilities across their customer base and discovered suspicious activity on two customer networks. Indicators of Compromise (IoCs) potentially indicative of successful exploitation of CVE-2025-0282 were identified as early as December 2024, 11 days before they had been publicly disclosed by Ivanti.

Case 1: December 2024

Authentication with a Privileged Credential

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024, when a customer device was observed logging into the network via SMB using the credential “svc_negbackups”, before authenticating with the credential “svc_negba” via RDP.

This likely represented a threat actor attempting to identify vulnerabilities within the system or application and escalate their privileges from a basic user account to a more privileged one. Darktrace / NETWORK recognized that the credential “svc_negbackups” was new for this device and therefore deemed it suspicious.

Darktrace / NETWORK’s detection of the unusual use of a new credential.
Figure 1: Darktrace / NETWORK’s detection of the unusual use of a new credential.

Likely Malicious File Download

Shortly after authentication with the privileged credential, Darktrace observed the device performing an SMB write to the C$ share, where a likely malicious executable file, ‘DeElevate64.exe’ was detected. While this is a legitimate Windows file, it can be abused by malicious actors for Dynamic-Link Library (DLL) sideloading, where malicious files are transferred onto other devices before executing malware. There have been external reports indicating that threat actors have utilized this technique when exploiting the Ivanti vulnerabilities [2].

Darktrace’s detection the SMB write of the likely malicious file ‘DeElevate64.exe’ on December 29, 2024.
Figure 2: Darktrace’s detection the SMB write of the likely malicious file ‘DeElevate64.exe’ on December 29, 2024.

Shortly after, a high volume of SMB login failures using the credential “svc_counteract-ext” was observed, suggesting potential brute forcing activity. The suspicious nature of this activity triggered an Enhanced Monitoring model alert that was escalated to Darktrace’s Security Operations Center (SOC) for further investigation and prompt notification, as the customer was subscribed to the Security Operations Support service.  Enhanced Monitoring are high-fidelity models detect activities that are more likely to be indicative of compromise

Suspicious Scanning and Internal Reconnaissance

Darktrace then went on to observe the device carrying out network scanning activity as well as anomalous ITaskScheduler activity. Threat actors can exploit the task scheduler to facilitate the initial or recurring execution of malicious code by a trusted system process, often with elevated permissions. The same device was also seen carrying out uncommon WMI activity.

Darktrace’s detection of a suspicious network scan from the compromised device.
Figure 3: Darktrace’s detection of a suspicious network scan from the compromised device.

Further information on the suspicious scanning activity retrieved by Cyber AI Analyst, including total number of connections and ports scanned.
Figure 4: Further information on the suspicious scanning activity retrieved by Cyber AI Analyst, including total number of connections and ports scanned.
Darktrace’s detection of a significant spike in WMI activity represented by DCE_RPC protocol request increases at the time, with little to no activity observed one week either side.
Figure 5: Darktrace’s detection of a significant spike in WMI activity represented by DCE_RPC protocol request increases at the time, with little to no activity observed one week either side.

Case 2: January 2025

Suspicious File Downloads

On January 13, 2025, Darktrace began to observe activity related to the exploitation of CVE-2025-0282  on the network of another customer, with one in particular device attempting to download likely malicious files.

Firstly, Darktrace observed the device making a GET request for the file “DeElevator64.dll” hosted on the IP 104.238.130[.]185. The device proceeded to download another file, this time “‘DeElevate64.exe”. from the same IP. This was followed by the download of “DeElevator64.dll”, similar to the case observed in December 2024. External reporting indicates that this DLL has been used by actors exploiting CVE-2025-0282 to sideload backdoor into infected systems [2]

Darktrace’s detection of the download of the suspicious file “DeElevator64.dll” on January 13, 2025.
Figure 6: Darktrace’s detection of the download of the suspicious file “DeElevator64.dll” on January 13, 2025.

Suspicious Internal Activity

Just like the previous case, on January 15, the same device was observed making numerous internal connections consistent with network scanning activity, as well as DCE-RPC requests.

Just a few minutes later, Darktrace again detected the use of a new administrative credential, observing the following details:

  • domain=REDACTED hostname=DESKTOP-1JIMIV3 auth_successful=T result=success ntlm_version=2 .

The hostname observed by Darktrace, “DESKTOP-1JIMIV3,” has also been identified by other external vendors and was associated with a remote computer name seen accessing compromised accounts [2].

Darktrace also observed the device performing an SMB write of an additional file, “to.bat,” which may have represented another malicious file loaded from the DLL files that the device had downloaded earlier. It is possible this represented the threat actor attempting to deploy a remote scheduled task.

Darktrace’s detection of SMB Write of the suspicious file “to.bat”.
Figure 7: Darktrace’s detection of SMB Write of the suspicious file “to.bat”.

Further investigation revealed that the device was likely a Veeam server, with its MAC address indicating it was a VMware device. It also appeared that the Veeam server was capturing activities referenced from the hostname DESKTOP-1JIMIV3. This may be analogous to the remote computer name reported by external researchers as accessing accounts [2]. However, this activity might also suggest that while the same threat actor and tools could be involved, they may be targeting a different vulnerability in this instance.

Autonomous Response

In this case, the customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device. This action allows a device to make its usual connections while blocking any that deviate from expected behavior. These mitigative actions by Darktrace ensured that the compromise was promptly halted, preventing any further damage to the customer’s environment.

Darktrace's Autonomous Response capability actively mitigating the suspicious internal connectivity.
Figure 8: Darktrace's Autonomous Response capability actively mitigating the suspicious internal connectivity.

Conclusion

If the previous blog in January 2024 was a stark reminder of the threat posed by malicious actors exploiting Internet-facing assets, the recent activities surrounding CVE-2025-0282 and CVE-2025-0283 emphasize this even further.

Based on the telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated .

These activities included the download of suspicious files such as “DeElevate64.exe” and “DeElevator64.dll” potentially used by attackers to sideload backdoors into infected systems. The suspicious hostname DESKTOP-1JIMIV3 was also observed and appears to be associated with a remote computer name seen accessing compromised accounts. These activities are far from exhaustive, and many more will undoubtedly be uncovered as threat actors evolve.

Fortunately, Darktrace was able to swiftly detect and respond to suspicious network activity linked to the latest Ivanti vulnerabilities, sometimes even before these vulnerabilities were publicly disclosed.

Credit to: Nahisha Nobregas, Senior Cyber Analyst, Emma Foulger, Principle Cyber Analyst, Ryan Trail, Analyst Content Lead and the Darktrace Threat Research Team

Appendices

Darktrace Model Detections

Case 1

·      Anomalous Connection / Unusual Admin SMB Session

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Internal / Unusual SMB Script Write

·      Anomalous File / Multiple EXE from Rare External Locations

·      Anomalous File / Script from Rare External Location

·      Compliance / SMB Drive Write

·      Device / Multiple Lateral Movement Model Alerts

·      Device / Network Range Scan

·      Device / Network Scan

·      Device / New or Uncommon WMI Activity

·      Device / RDP Scan

·      Device / Suspicious Network Scan Activity

·      Device / Suspicious SMB Scanning Activity

·      User / New Admin Credentials on Client

·      User / New Admin Credentials on Server 

Case 2

·      Anomalous Connection / Unusual Admin SMB Session

·      Anomalous Connection / Unusual Admin RDP Session

·      Compliance / SMB Drive Write

·      Device / Multiple Lateral Movement Model Alerts

·      Device / SMB Lateral Movement

·      Device / Possible SMB/NTLM Brute Force

·      Device / Suspicious SMB Scanning Activity

·      Device / Network Scan

·      Device / RDP Scan

·      Device / Large Number of Model Alerts

·      Device / Anomalous ITaskScheduler Activity

·      Device / Suspicious Network Scan Activity

·      Device / New or Uncommon WMI Activity

List of IoCs Possible IoCs:

·      DeElevator64.dll

·      deelevator64.dll

·      DeElevate64.exe

·      deelevator64.dll

·      deelevate64.exe

·      to.bat

Mid-high confidence IoCs:

-       104.238.130[.]185

-       http://104.238.130[.]185/DeElevate64.exe

-       http://104.238.130[.]185/DeElevator64.dll

-       DESKTOP-1JIMIV3

References:

1.     https://www.ivanti.com/blog/security-update-ivanti-connect-secure-policy-secure-and-neurons-for-zta-gateways

2.     https://unit42.paloaltonetworks.com/threat-brief-ivanti-cve-2025-0282-cve-2025-0283/

3.     https://www.proofpoint.com/uk/blog/identity-threat-defense/privilege-escalation-attack#:~:text=In%20this%20approach%2C%20attackers%20exploit,handing%20over%20their%20login%20credentials

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Hugh Turnbull
Cyber Analyst

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January 7, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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January 5, 2026

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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