The most important cybersecurity impact of artificial intelligence is not that attackers can write better phishing emails or automate parts of their workflow. It is that AI is changing the speed, scale, and decision-making dynamics of cyberattacks.
That creates a problem many organizations have not yet fully confronted: most cyber governance and incident response models were designed for human-speed attacks.
For years, security teams operated under a familiar sequence. Detect suspicious activity. Investigate. Validate the threat. Escalate to leadership. Decide on containment. Communicate with stakeholders. That model still has value, but it assumes defenders have enough time to build confidence before taking material action.
AI-enabled attacks challenge that assumption.
Adversaries can now use AI to accelerate reconnaissance, generate highly personalized social engineering, modify malware, test payloads, summarize stolen data, identify vulnerabilities, and adapt tactics quickly. These capabilities compress the time between initial targeting and business impact. In some cases, defenders may not have the luxury of waiting for full clarity before escalating or containing an incident.
This is why AI risk is no longer only a technical issue. It is a governance issue, an identity issue, and an executive decision-making issue.
The AI Security Gap Is a Timing Gap
Much of the conversation around AI in cybersecurity focuses on tools: how defenders can use AI to detect threats faster, how attackers can use AI to automate campaigns, and how organizations can secure AI systems themselves.
Those are important questions. But they do not capture the full challenge.
The deeper issue is timing.
Traditional incident response assumes that teams can move through a relatively orderly process of investigation, escalation, and containment. AI-enabled threats can shorten that window. Attackers can iterate faster. Social engineering can become more convincing. Malware can be adjusted more quickly. Phishing campaigns can be tailored to specific employees, executives, and business processes at scale.
In this environment, the critical question becomes: can the organization make the right decision quickly enough?
That question cannot be answered by the security operations center alone. It requires alignment across the CISO, executive leadership, legal, communications, risk, compliance, and the board. Sygnia’s recent CISO survey on state of IR readiness found that 90% of respondents would struggle to coordinate stakeholders during a major incident, and 75% say uncertainty around legal and comms involvement delays decision-making during incident. Clearly, there is work to be done on improving incident response cross-functional coordination
Traditional Assumptions Are Starting to Break
Organizations should reassess several long-standing assumptions that have shaped their cybersecurity programs.
These shifts do not make existing controls obsolete. But they do expose where legacy controls, playbooks, and reporting models may no longer be sufficient.
Social Engineering Has Become an Industrial Process
AI has made social engineering more scalable and more believable.
Attackers no longer need to rely on generic emails or clumsy impersonation attempts. They can use publicly available information, breached data, social media posts, company websites, executive interviews, job postings, and internal context from previous compromises to craft messages that sound credible.
A finance employee may receive a payment request that appears to match the CFO’s writing style. A help desk analyst may receive a convincing message from someone impersonating a senior executive. A regional office may be targeted in its local language with references to real suppliers, business priorities, or current events.
This undermines a basic assumption in enterprise security: that people can reliably distinguish legitimate communication from malicious communication based on tone, style, or context.
They increasingly cannot.
A recent report found that 82.6% of the phishing emails 1analyzed between September 15, 2024 and February 14, 2025 exhibited some use of AI.
The operational lesson is still clear: social engineering defenses must move beyond traditional awareness training alone.
Security leaders should normalize verification procedures for sensitive actions, including:
- Payment approvals
- Vendor bank account changes
- Credential resets
- Privileged access requests
- Confidential data transfers
- Executive instructions sent through email, voice, chat, or video
Verification should not be treated as distrust. It should be treated as a standard control in an environment where identity signals are easier to fake.
Executive Identity Is Now Part of the Attack Surface
Executives have always been targets. In the AI era, they are also becoming instruments of attack.
Synthetic voice, deepfake video, and AI-generated writing make it easier to impersonate executives convincingly. Attackers can reproduce leadership tone, urgency, and business context to pressure employees into bypassing normal controls.
This creates a new challenge for organizations: protecting executive identity is no longer only about securing executive accounts. It is about preventing executive likeness, communication patterns, and authority from being abused.
Addressing this risk requires a combination of technical controls and organizational discipline. Organizations should establish clear verification requirements for urgent executive requests, require out-of-band confirmation through trusted communication channels, and limit high-risk approvals conducted through informal messaging platforms. Employees who are most likely to receive executive requests, including executive assistants, finance teams, IT help desks, and legal teams should be trained to recognize and respond to impersonation attempts. Incident response plans should also include scenarios involving deepfake or synthetic executive communications so teams can react quickly when an incident occurs.
Most importantly, verification procedures must apply to everyone, including the CEO and board members. Attackers thrive on ambiguity, hierarchy, and urgency. Consistent processes help eliminate all three.
AI Adoption Can Expand the Defender’s Attack Surface
While organizations are focused on attacker use of AI, they must also examine their own AI adoption.
AI is being embedded into productivity suites, customer service tools, software development workflows, security platforms, analytics environments, and third-party SaaS applications. These systems may access sensitive data, automate business processes, interact with users, or influence operational decisions.
That creates new risk questions:
- What enterprise data can AI tools access?
- Are prompts and outputs logged and monitored?
- Can AI systems trigger business actions?
- Who approves new AI tools and integrations?
- How are third-party AI providers assessed?
- Are employees using unsanctioned AI tools?
- What happens if an AI-enabled workflow is manipulated?
- How would the organization investigate AI misuse?
Many vendor risk programs were not designed to evaluate AI-specific issues such as prompt injection, model behavior, data leakage, autonomous agents, or AI-enabled decision workflows. As a result, organizations may be taking on AI exposure without fully understanding where that exposure resides.
AI governance should therefore be integrated into cybersecurity governance, not treated as a separate innovation track.
Detection Must Shift From Indicators to Behavior
Known indicators of compromise still matter, but they are less sufficient against adaptive threats.
AI can help attackers vary language, infrastructure, payloads, timing, and targeting. This makes it harder for defenders to rely primarily on static indicators or previously observed patterns.
Detection strategies must become more behavioral.
Instead of asking only, “Have we seen this indicator before?” defenders need to ask:
- Is this login behavior normal for this user?
- Is this data access pattern unusual?
- Is this SaaS activity consistent with business context?
- Is this endpoint behavior expected?
- Is this communication pattern typical?
- Is this privilege escalation aligned with normal operations?
This requires stronger telemetry across identity, endpoint, cloud, email, collaboration tools, SaaS platforms, and network activity. It also requires faster correlation across these environments.
The goal is not to replace analysts with AI. The goal is to help analysts see abnormal behavior sooner and make better decisions faster.
Incident Response Needs to Be Rehearsed for Uncertainty
Many incident response plans assume a relatively linear sequence of events. AI-enabled attacks may not unfold that way. Security teams may face a scenario where the organization is under active attack, the attacker's identity is unknown, executive communications may be spoofed, business operations may be disrupted, and leadership must decide whether to contain systems before the full investigation is complete.
That is not only a technical problem. It is a decision-making problem. As a result, tabletop exercises should be designed to test how organizations respond under compressed timelines and with incomplete information. Effective scenarios should incorporate AI-generated phishing targeting executives and finance teams, synthetic voice or deepfake impersonation, compromised SaaS platforms, rapid credential abuse, AI-assisted business email compromise, and data exposure through AI-enabled tools. Organizations should also consider the impact of third-party AI platform compromises and challenge participants to make containment decisions before attribution has been confirmed.
Crisis communications should be tested as well, particularly in situations where the authenticity of executive communications is uncertain. The most valuable exercises will not simply assess whether the security team can detect an incident. They will evaluate whether the organization can make sound decisions when evidence is incomplete, trust is uncertain, and time is working against them.
Boards Need AI Risk Fluency, Not Technical Depth
Boards and executives do not need to understand every technical detail of machine learning, large language models, or adversarial AI. But they do need to understand how AI changes cyber risk.
At a minimum, leadership should understand that AI can:
- Increase the scale and believability of social engineering
- Reduce the time available for investigation and containment
- Create new third-party and data governance risks
- Undermine traditional identity verification assumptions
- Increase the importance of behavioral detection
- Require faster escalation and decision-making
This level of fluency matters because many AI-era cyber decisions will not be purely technical. They may involve business interruption, legal exposure, customer communications, regulatory notification, vendor management, and executive accountability.
If governance structures are not updated, high-level cyber risk decisions may become disconnected from operational reality.
What Security Leaders Should Reassess Now
Organizations should begin with a practical assessment of their AI readiness, examining both the opportunities AI creates and the risks it introduces across the enterprise. This review should evaluate how AI is currently being used, identify both approved and unapproved AI tools, and assess whether sensitive information could be exposed through AI systems. It should also examine the effectiveness of identity and access controls for AI-enabled workflows, as well as the organization's ability to capture and retain prompt, output, and activity logs for security and investigative purposes.
Beyond internal usage, organizations should assess the risks associated with third-party AI platforms and AI-enabled SaaS providers. Governance reviews should include executive verification procedures, the effectiveness of behavioral detection capabilities, incident escalation thresholds, and the clarity of containment authority and decision-making responsibilities during a crisis. Regular tabletop exercises should test the organization's readiness for AI-enabled attack scenarios, while incident response retainers and surge capacity arrangements should be reviewed to ensure adequate support during a major event.
The objective is not to create a separate AI security program disconnected from the broader cybersecurity strategy. The objective is to evolve existing governance, detection, and response capabilities, so they remain effective against AI-enabled threats and the increasingly complex environments in which organizations operate.
The Bottom Line
AI is changing the tempo of cyber risk. Attackers can move faster, personalize deception more effectively, and adapt tactics with less effort. Defenders can also use AI to improve detection, triage, and response. But technology alone will not solve the core problem.
The organizations that fare best will not necessarily be the ones that adopt AI the fastest. They will be the ones that recalibrate governance, identity, detection, and incident response quickly enough to withstand AI-speed attacks.
That effort begins with clear ownership and accountability. Organizations should establish cross-functional governance structures with executive sponsorship and defined decision-making authority. AI should be treated as an accelerator rather than a replacement for human expertise, embedded into structured workflows that preserve oversight, judgement, and accountability. At the same time, security leaders should focus on closing visibility gaps across IT, cloud, SaaS, and operational technology environments, ensuring that security teams can understand and respond to threats wherever they emerge.
Risk prioritization must also evolve. Rather than relying solely on technical severity ratings, organizations should reassess critical and high-risk findings through the lens of business impact, operational dependencies, organizational context, and likely adversary behavior. As vulnerabilities become weaponized more quickly, response processes must be equally agile.
Organizations should establish repeatable, high-urgency response pathways that support rapid scope validation, business impact assessment, ownership determination, deployment of compensating controls, executive communications, and post-remediation verification.
Equally important is the ability to activate a full-scale incident response effort when required. This includes not only digital forensics and incident response capabilities, but also crisis communications, negotiation support, system recovery planning, and the broader coordination required to manage complex cyber crises.
In the AI era, resilience depends on more than better tools. It depends on understanding which risks matter most, assigning clear ownership, responding at the speed of the threat, and preparing leadership teams to make high-consequence decisions before the full picture is available.
About the author: Guy Segal serves as Chief Executive Officer at Sygnia, where he oversees the company’s strategy, operations, and continued growth as a global leader in incident response and cyber readiness. With more than 25 years of experience in cybersecurity, technology, and business leadership, Guy has played a central role in shaping Sygnia’s trajectory since joining in 2020. He has held senior leadership positions including Head of APJ, where he expanded Sygnia’s presence in Asia Pacific and Japan; initiator of the company’s Managed Detection and Response (MDR) practice; and leader of Corporate Development, driving strategic growth initiatives across the business. Guy has also personally led some of the most complex and high-stakes cyber incidents Sygnia has handled, giving him unique insight into the challenges clients face and the solutions needed to overcome them.
Guy Segal — CEO at Sygnia https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgl4WHXBqMMWFpAq6MmyYAn336gX2uQtZeSMsIp0c1HZ40qEqfKdCCnu2PlsIipd5WW0NQLKAFu9YzxxfGknEXhna_8czeK4CXG-Ev9YUUInvp_C6GSUCCzMdu6KL1dCVPAIgg5limA1nPkq5VvxwIpreqv8tn7piXBCa2KbFQ04IniOo8hwELs7EE5BFI/s1600/guy.png
