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March 1, 2024

Navigating Email Security Gaps Beyond DMARC

Discover Darktrace's strategies for navigating email security gaps beyond DMARC. Enhance your defenses with advanced threat detection and mitigation.
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering
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01
Mar 2024

Email threat landscape  

Email has consistently ranked among the most targeted attack vectors, given its ubiquity and criticality to business operations. From September to December 2023, 10.4 million phishing emails were detected across Darktrace’s customer fleet demonstrating the frequency of attempted email-based attacks.

Businesses are searching for ways to harden their email security posture alongside email providers who are aiming to reduce malicious emails traversing their infrastructure, affecting their clients. Domain-based Message Authentication (DMARC) is a useful industry-wide protocol organizations can leverage to move towards these goals.  

What is DMARC?

DMARC is an email authentication protocol designed to enhance the security of email communication.

Major email service providers Google and Yahoo recently made the protocol mandatory for bulk senders in an effort to make inboxes safer worldwide. The new requirements demonstrate an increasing need for a standardized solution as misconfigured or nonexistent authentication systems continue to allow threat actors to evade detection and leverage the legitimate reputation of third parties.  

DMARC is a powerful tool that allows email administrators to confidently identify and stop certain spoofed emails; however, more organizations must implement the standard for it to reach its full potential. The success and effectiveness of DMARC is dependent on broad adoption of the standard – by organizations of all sizes.  

How does DMARC work?

DMARC builds on two key authentication technologies, Sender Policy Framework (SPF) and DomainKeys Identified Mail (DKIM) and helps to significantly improve their ability to prevent domain spoofing. SPF verifies that a sender’s IP address is authorized to send emails on behalf of a particular domain and DKIM ensures integrity of email content by providing a verifiable digital signature.  

DMARC adds to this by allowing domain owners to publish policies that set expectations for how SPF and DKIM verification checks relate to email addresses presented to users and whose authenticity the receiving mail server is looking to establish.  

These policies work in tandem to help authenticate email senders by verifying the emails are from the domain they say they are, working to prevent domain spoofing attacks. Key benefits of DMARC include:

  1. Phishing protection DMARC protects against direct domain spoofing in which a threat actor impersonates a legitimate domain, a common phishing technique threat actors use to trick employees to obtain sensitive information such as privileged credentials, bank information, etc.  
  2. Improving brand reputation: As DMARC helps to prevent impersonation of domains, it stands to maintain and increase an organization’s brand reputation. Additionally, as organizational reputation improves, so will the deliverability of emails.
  3. Increased visibility: DMARC provides enhanced visibility into email communication channels, including reports of all emails sent on behalf of your domain. This allows security teams to identify shadow-IT and any unauthorized parties using their domain.

Understanding DMARC’s Limitations

DMARC is often positioned as a way for organizations to ‘solve’ their email security problems, however, 65% of the phishing emails observed by Darktrace successfully passed DMARC verification, indicating that a significant number of threat actors are capable of manipulating email security and authentication systems in their exploits. While DMARC is a valuable tool in the fight against email-based attacks, the evolving threat landscape demands a closer look at its limitations.  

As threat actors continue to innovate, improving their stealth and evasion tactics, the number of attacks with valid DMARC authentication will only continue to increase in volume and sophistication. These can include:

  1. Phishing attacks that leverage non-spoofed domains: DMARC allows an organization to protect the domains that they own, preventing threat actors from being able to send phishing emails from their domains. However, threat actors will often create and use ‘look-a-like’ domains that closely resemble an organization’s domain to dupe users. 3% of the phishing emails identified by Darktrace utilized newly created domains, demonstrating shifting tactics.  
  2. Email Account Takeovers: If a threat actor gains access to a user’s email account through other social engineering means such as credential stuffing, they can then send phishing emails from the legitimate domain to pursue further attacks. Even though these emails are malicious, DMARC would not identify them as such because they are coming from an authorized domain or sender.  

Organizations must also ensure their inbound analysis of emails is not skewed by successful DMARC authentication. Security teams cannot inherently trust emails that pass DMARC, because the source cannot always be legitimized, like in the event of an account takeover. If a threat actor gains access to an authenticated email account, emails sent by the threat actor from that account will pass DMARC – however the contents of that email may be malicious. Sender behavior must be continuously evaluated and vetted in real time as past communication history and validated DMARC cannot be solely relied upon amid an ever-changing threat landscape.  

Security teams should lean on other security measures, such as anomaly detection tools that can identify suspicious emails without relying on historical attack rules and static data. While DMARC is not a silver bullet for email security, it is nevertheless foundational in helping organizations protect their brand identity and must be viewed as an essential layer in an organization's overall cyber security strategy.  

Implementing DMARC

Despite the criticality of DMARC for preserving brand reputation and trust, adoption of the standard has been inconsistent. DMARC can be complex to implement with many organizations lacking the time required to understand and successfully implement the standard. Because of this, DMARC set-up is often outsourced, giving security and infrastructure teams little to no visibility into or control of the process.  

Implementation of DMARC is only the start of this process, as DMARC reports must be consistently monitored to ensure organizations have visibility into who is sending mail from their domain, the volume of mail being sent and whether the mail is passing authentication protocols. This process can be time consuming for security teams who are already faced with mounting responsibilities, tight budgets, and personnel shortages. These complexities unfortunately delay organizations from using DMARC – especially as many today still view it as a ‘nice to have’ rather than an essential.  

With the potential complexities of the DMARC implementation process, there are many ways security and infrastructure teams can still successfully roll out the standard. Initial implementation should start with monitoring, policy adjustment and then enforcement. As business changes over time, DMARC should be reviewed regularly to ensure ongoing protection and maintain domain reputation.

The Future of Email Security

As email-based attacks continue to rise, the industry must recognize the importance of driving adoption of foundational email authentication protocols. To do this, a new and innovative approach to DMARC is needed. DMARC products must evolve to better support organizations throughout the ongoing DMARC monitoring process, rather than just initial implementation. These products must also be able to share intelligence across an organization’s security stack, extending beyond email security tools. Integration across these products and tools will help organizations optimize their posture, ensuring deep understanding of their domain and increased visibility across the entire enterprise.

DMARC is critical in protecting brand identity and mitigating exact-domain based attacks. However, organizations must understand DMARC’s unique benefits and limitations to ensure their inboxes are fully protected. In today’s evolving threat landscape, organizations require a robust, multi-layered approach to stop email threats – in inbound mail and beyond. Email threats have evolved – its time security does too.

Join Darktrace on 9 April for a virtual event to explore the latest innovations needed to get ahead of the rapidly evolving threat landscape. Register today to hear more about our latest innovations coming to Darktrace’s offerings. For additional insights check out Darktrace’s 2023 End of Year Threat Report.

Credit to Carlos Gray and Stephen Pickman for their contribution to this blog

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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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