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February 6, 2025

Reimagining Your SOC: Unlocking a Proactive State of Security

Reimagining your SOC Part 3/3: This blog explores the challenges security professionals face in managing cyber risk, evaluates current market solutions, and outlines strategies for building a proactive security posture.
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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.
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06
Feb 2025

Part 1: How to Achieve Proactive Network Security

Part 2: Overcoming Alert Fatigue with AI-Led Investigations  

While the success of a SOC team is often measured through incident management effectiveness (E.g MTTD, MTTR), a true measure of maturity is the reduction of annual security incidents.

Organizations face an increasing number of alerts each year, yet the best SOC teams place focus on proactive operations which don’t reduce the threshold for what becomes an incident but targets the source risks that prevent them entirely.

Freeing up time to focus on cyber risk management is a challenge in and of itself, we cover this in the previous two blogs in this series (see above). However, when the time comes to manage risk, there are several challenges that are unique when compared to detection & response functions within cybersecurity.

Why do cyber risks matter?

While the volume of reported CVEs is increasing at an alarming rate[1], determining the criticality of each vulnerability is becoming increasingly challenging, especially when the likelihood and impact may be different for each organization. Yet vulnerabilities have stood as an important signpost in traditional security and mitigation strategies. Now, without clear prioritization, potentially severe risks may go unreported, leaving organizations exposed to significant threats.

Vulnerabilities also represent just one area of potential risks. Cyberattacks are no longer confined to a single technology type. They now traverse various platforms, including cloud services, email systems, and networks. As technology infrastructure continues to expand, so does the attack surface, making comprehensive visibility across all technology types essential for reducing risk and preventing multi-vector attacks.

However, achieving this visibility is increasingly difficult as infrastructure grows and the cyber risk market remains oversaturated. This visibility challenge extends beyond technology to include personnel and individual cyber hygiene which can still exacerbate broader cyberattacks whether malicious or not.

Organizations must adopt a holistic approach to preventative security. This includes improving visibility across all technology types, addressing human risks, and mobilizing swiftly against emerging security gaps.

“By 2026, 60% of cybersecurity functions will implement business-impact-focused risk assessment methods, aligning cybersecurity strategies with organizational objectives.” [2]

The costs of a fragmented approach

siloed preventative security measures or technologies
Figure 1: Organizations may have a combination of siloed preventative security measures or technologies in place

Unlike other security tools (like SIEM, NDR or SOAR) which contain an established set of capabilities, cyber risk reduction has not traditionally been defined by a single market, rather a variety of products and practices that each provide their own value and are overwhelming if too many are adopted. Just some examples include:

  • Threat and Vulnerability management: Leverages threat intelligence, CVEs and asset management; however, leaves teams with significant patching workflows, ignores business & human factors and is reliant on the speed of teams to keep up with each passing update.  
  • Continuous Controls Monitoring (CCM): Automatically audits the effectiveness of security controls based on industry frameworks but requires careful prioritization and human calculations to set-up effectively. Focuses solely on mobilization.
  • Breach and Attack Simulation (BAS): Automates security posture testing through mock scenarios but require previous prioritization and might not tell you how your specific technologies can be mitigated to reduce that risk.
  • Posture Management technologies: Siloed approaches across Cloud, SaaS, Data Security and even Gen AI that reactively assess misconfigurations and suggest improvements but with only industry frameworks to validate the importance of the risks.
  • Red teaming & Penetration testing: Required by several regulations including (GDPR, HIPPA, PCI, DSS), many organizations hire 'red teams' to perform real breaches in trusted conditions. Penetration tests reveal many flaws, but are not continuous, requiring third-party input and producing long to-do lists with input of broader business risk dependent on the cost of the service.
  • Third-party auditors: Organizations also use third-party auditors to identify assets with vulnerabilities, grade compliance, and recommend improvements. At best, these exercises become tick-box exercises for companies to stay in compliance with the responsibility still on the client to perform further discovery and actioning.

Many of these individual solutions on the market offer simple enhancement, or an automated version of an existing human security task. Ultimately, they lack an understanding of the most critical assets at your organization and are limited in scope, only working in a specific technology area or with the data you provide.

Even when these strategies are complete, implementation of the results require resources, coordination, and buy-in from IT, cybersecurity, and compliance departments. Given the nature of modern business structures, this can be labor and time intensive as responsibilities are shared by organizational segmentation spread across IT, governance, risk and compliance (GRC), and security teams.

Prioritize your true cyber risk with a CTEM approach

Organizations with robust security programs benefit from well-defined policies, standards, key risk indicators (KRIs), and operational metrics, making it easier to measure and report cyber risk accurately.

Implementing a framework like Gartner’s CTEM (Continuous Threat Exposure Management) can help governance by defining the most relevant risks to each organization and which specific solutions meet your improvement needs.

This five-step approach—scoping, discovery, prioritization, validation, and mobilization—encourages focused management cycles, better delegation of responsibilities and a firm emphasis on validating potential risks through technological methods like attack path modeling or breach and attack simulation to add credibility.

Implementing CTEM requires expertise and structure. This begins with an exposure management solution developed uniquely alongside a core threat detection and response offering, to provide visibility of an organization’s most critical risks, whilst linking directly to their incident-based workflows.

“By 2026, organizations prioritizing their security investments, based on a continuous threat exposure management program, will realize a two-third reduction in breaches.” [3]

Achieving a proactive security posture across the whole estate

Unlike conventional tools that focus on isolated risks, Darktrace / Proactive Exposure Management breaks down traditional barriers. Teams can define risk scopes with full, prioritized visibility of the critical risks between: IT/OT networks, email, Active Directory, cloud resources, operational groups, (or even the external attack surface by integrating with Darktrace / Attack Surface Management).

Our innovative, AI-led risk discovery provides a view that mirrors actual attacker methodologies. It does this through advanced algorithms that determine risk based on business importance, rather than traditional device-type prioritization. By implementing a sophisticated damage assessment methodology, security teams don’t just prioritize via severity but instead, the inherent impact, damage, weakness and external exposure of an asset or user.

These calculations also revolutionize vulnerability management by combining industry standard CVE measurements with that organization-specific context to ensure patch management efforts are efficient, rather than an endless list.

Darktrace also integrates MITRE ATT&CK framework mappings to connect all risks through attack path modeling. This offers validation to our AI’s scoring by presenting real world incident scenarios that could occur across your technologies, and the actionable mitigations to mobilize against them.

For those human choke points, security may also deploy targeted phishing engagements. These send real but harmless email ‘attacks’ to test employee susceptibility, strengthening your ability to identify weak points in your security posture, while informing broader governance strategies.

Combining risk with live detection and response

Together, each of these capabilities let teams take the best steps towards reducing risk and the volume of incidents they face. However, getting proactive also sharpens your ability to handle live threats if they occur.  

During real incidents Darktrace users can quickly evaluate the potential impact of affected assets, create their own risk detections based on internal policies, strengthen their autonomous response along critical attack paths, or even see the possible stage of the next attack.

By continually ingesting risk information into live triage workflows, security teams will develop a proactive-first mindset, prioritizing the assets and alerts that have the most impact to the business. This lets them utilize their resource in the most efficient way, freeing up even more time for risk management, mitigation and ensuring continuity for the business.

Whether your organization is laying the foundation for a cybersecurity program or enhancing an advanced one, Darktrace’s self-learning AI adapts to your needs:

  • Foundational stage: For organizations establishing visibility and automating detection and response.
  • Integrated stage: For teams expanding coverage across domains and consolidating tools for simplicity.
  • Proactive stage: For mature security programs enhancing posture with vulnerability management and risk prioritization.

The Darktrace ActiveAI Security Platform empowers security teams to adopt a preventative defense strategy by using Cyber AI Analyst and autonomous response to fuel quicker triage, incident handling and give time back for proactive efforts designed around business impact. The platform encapsulates the critical capabilities that help organizations be proactive and stay ahead of evolving threats.

darktrace proactive exposure management solution brief reduce risk cyber risk

Download the solution brief

Maximize security visibility and reduce risk:

  • Unify risk exposure across all technologies with AI-driven scoring for CVEs, human communications, and architectures.
  • Gain cost and ROI insights on CVE risks, breach costs, patch latency, and blind spots.
  • Strengthen employee awareness with targeted phishing simulations and training.
  • Align proactive and reactive security by assessing device compromises and prevention strategies.
  • Reduce risk with tailored guidance that delivers maximum impact with minimal effort.

Take control of your security posture today. Download here!

References

[1] https://nvd.nist.gov/vuln/search, Search all, Statistics, Total matches By Year 2023 against 2024

[2] https://www.gartner.com/en/documents/5598859

[3] https://www.gartner.com/en/articles/how-to-manage-cybersecurity-threats-not-episodes

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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.
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June 24, 2026

A New Security Challenge: The Curious Case of Prompt Language Analysis

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Why prompt analysis is emerging as a key AI security challenge

If securing AI has been one of the defining cybersecurity conversations of the past year, prompt analysis is quickly becoming one of its most interesting frontiers.

Security leaders are under pressure to understand how AI is being used across the business. In some organizations, that means governing employee use of chatbots. In others, it means overseeing copilots embedded into SaaS platforms, monitoring coding assistants, or assessing the growing footprint of autonomous agents. However different these use cases may appear on the surface, they share a common factor: humans and machines are usually interacting with enterprise systems through language.  

How prompt language differs from traditional security telemetry

For years, defenders have become used to working with familiar forms of telemetry: email traffic, network connections, API calls, endpoint processes, authentication events. Prompt language is different. It is not simply another log source. It is an expression of intent, instruction, curiosity, urgency, and sometimes manipulation. It reflects the end-goal of a user or agent, but not always with enough surrounding context to interpret the risk correctly.

Why existing security approaches only partially explain prompt risk

A growing number of vendors are approaching the task of securing AI from the angle they know best. Perimeter vendors are extending web or browser controls into AI usage. Identity vendors are emphasizing agent permissions and access governance. Data security and DLP providers are focusing on content inspection and exfiltration risk. All of these perspectives matter, but individually can’t fully explain the problem.

The challenge with securing AI is not just that a new application category has emerged. It is that language has become a new operating layer in the enterprise.

Employees now use prompts to summarize documents, generate code, analyze spreadsheets, query internal knowledge, and trigger multi-step actions through agents. In each case, prompt language acts as the interface between human intent and machine execution. That makes prompts incredibly valuable from a security perspective as they can hint at misuse, policy violations, data exposure, or attempts to circumvent controls. However, they can also be deeply ambiguous when viewed in isolation. That ambiguity is the heart of the issue.

Prompts as behavioral signals, not just text to classify

A prompt by itself tells you what was asked. It does not necessarily tell you whether the request is expected, risky, accidental, or entirely legitimate in context. Two nearly identical prompts can carry very different meanings depending on the role and function of who issued them, what systems they can access, and what actions followed. In other words, prompts are not just text to classify. They are behavioral signals to interpret.

Example: How context changes prompt risk entirely

Consider a common enterprise scenario. An employee is pulled into a new project with an aggressive deadline. Almost overnight, their use of AI tools spikes. They begin prompting more frequently, working across unfamiliar documents, querying new data sources, and interacting with more systems than usual to accelerate delivery. Viewed narrowly, this may look suspicious. Prompt volume increases, file access patterns change, API and SaaS activity rise. From some vantage points, it may resemble insider risk or unmanaged AI usage.

But now add context. Imagine that, earlier that day, the employee received instructions from a senior leader asking them to support a time-sensitive initiative. Their communication history shows that this leader is a legitimate reporting-line superior. Their recent collaboration patterns align with the new project team. Their subsequent activity, while unusual for that individual’s baseline, is consistent with the business task they were assigned.

What initially looked like a risk event may actually be a normal response to business pressure. Without the surrounding context of communication, organizational relationships, and broader behavioral patterns, prompt activity alone could generate more noise than insight.

The reverse is also true. A prompt may appear benign on the surface while the context around it suggests elevated risk. A request that seems routine could originate from a compromised user, a newly connected external agent, a shadow AI workflow, or a user acting outside their normal role. The language itself may not contain anything obviously malicious, but the surrounding conditions may tell a very different story.

What security teams need to analyze prompts effectively

The future of prompt analysis is not just about understanding language. It is about understanding language in context.

To do that well, security teams need more than prompt inspection. They need to understand:

  • Who is issuing the prompt, whether human or agent
  • How that identity normally behaves across the enterprise
  • What systems, data, and workflows are connected to the interaction
  • Which relationships and communications explain the surrounding activity
  • Whether the downstream actions align with expected business behavior

When those layers are absent, prompt analysis can become another isolated control surface: useful in theory, but limited in practice. Security teams may detect unusual wording but miss the operational function behind it, overreact to benign changes in behavior, or miss subtle misuse because the prompt itself did not appear dangerous.

How organizations should think about prompt analysis going forward

Security teams have seen this pattern before. In the cloud, posture without runtime context left important gaps. In identity, access control without behavioral understanding missed misuse that looked legitimate on paper. In data security, content inspection without business context often created friction without resolving risk. AI is exposing the same lesson again: controls are strongest when they are coordinated, not isolated. As organizations work to secure AI and identify gaps across their security operations, prompt analysis will become an increasingly important source of insight, but only as part of a broader strategy.

Prompt analysis will undoubtedly become more common, as prompts are one of the clearest windows into how people and agents are using AI systems. However, what matters most is not simply collecting prompts or filtering dangerous phrases, but being able to place that language inside a wider behavioral and operational picture.

Organizations that already have a broader understanding of how work gets done across the enterprise will be better positioned to make sense of prompt language as this category matures. They will be better able to distinguish urgency from abuse, experimentation from exfiltration, and productive AI adoption from hidden risk.

Figure 1: Darktrace / SECURE AI reconstructs the full sequence of events, showing every user and agent interaction in context, with risky prompts highlighted and categorized, including PII, sensitive data, and other policy violations.

At Darktrace, this is the key lesson emerging from the market: prompt language does matter, but it does not stand alone. It is most valuable when treated as a new behavioral input that can enrich understanding across the enterprise, not as a self-contained source of truth.

Why prompts become less useful when analyzed in isolation

The curious case of prompt language analysis, then, is this: the more important prompts become, the less useful they are in a vacuum.

The real opportunity is not just to see what was asked. It is to understand why it was asked, what it meant in that moment, and what happened next.

For a deeper look at how organizations are approaching this challenge from the strengths of prompt analysis to its limitations in isolation see Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches, which expands on the role prompt-level controls play within a broader, context-driven security strategy.

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About the author
Nabil Zoldjalali
VP, Field CISO

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June 23, 2026

Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations

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Darktrace joins the OpenAI Daybreak Cyber Partner Program

Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.

This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.  

At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.  

Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.  

That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.  

In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.

That is the opportunity we see in partnering with OpenAI.  

What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining

The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.

For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.  

By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.  

This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.  

Why the Darktrace and OpenAI partnership matters for defenders

Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.

That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.

Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.

At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.  

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