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Cybersecurity in the Age of AI: Emerging Risks & How to Stay Ahead in 2026

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Cybersecurity in the Age of AI: Emerging Risks & How to Stay Ahead in 2026

Manoj Sharma

Manoj Sharma

Founder & Lead Coach · CISSP, CCSP, CISM, CRISC

Published 12 Jan 2026Updated 20 Jul 20268 min read271 views

Quick Answer

How is AI changing cybersecurity, and what are the emerging risks organisations must manage?

AI in cybersecurity is both a power multiplier and a risk multiplier — it does not replace security fundamentals, it amplifies them. Attackers use AI for personalised phishing, polymorphic malware, deepfakes, automated attacks, evasion, adaptive threats and faster zero-days; a 2025 Gartner survey found roughly 62% of organisations had faced a deepfake attack in the prior year. Defenders use AI to analyse huge datasets, detect anomalies and automate triage — IBM research shows organisations using AI and automation contain breaches 100+ days faster. A newer risk is securing AI itself: prompt injection tops the OWASP Top 10 for LLM Applications, and agentic AI adds behaviour hijacking, tool misuse and privilege abuse. Governance is the gap — most breached organisations with AI incidents had no AI governance policy. Key frameworks: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, EU AI Act. Cybernous, led by instructor Manoj Sharma, runs the GenAI Expert (GAESP) programme alongside CISSP and CISM coaching.

AI in cybersecurity is both a power multiplier and a risk multiplier. In safe hands, it helps teams detect threats faster and respond smarter. In the wrong hands, it lets attackers scale phishing, automate exploitation and evade defences.

This is no longer a forward-looking debate. In 2026, AI sits at the centre of both attack and defence: the World Economic Forum's cyber research finds the overwhelming majority of leaders now name AI the single biggest driver of change in cybersecurity, and organisations that use AI and automation in their security operations have been shown to contain breaches dramatically faster than those that don't. Let's look honestly at both sides.

Core Reality

AI doesn't replace security fundamentals. It amplifies them. Weak identity, weak logging, weak patching — AI just helps attackers exploit those gaps faster. If your basics are shaky, adding AI to the defence stack will not save you; it will simply produce faster alerts about a breach you were always going to suffer.

AI-Powered Threats in Cybersecurity

Attackers use AI to increase speed, scale and deception. These are the threat categories every security team should have on its map.

AI-Powered ThreatWhat It Looks Like
Sophisticated phishingHighly personalised, fluent messages for fraud and credential theft — no more spelling-mistake tells
Malware generationPolymorphic malware that mutates to evade signature detection
DeepfakesSynthetic video and audio used for impersonation and social engineering
Automated attacksLarge-scale scanning, probing and exploitation at machine speed
Evasion techniquesBypassing rule- and signature-based controls
Data breach triageAnalysing stolen data to surface the highest-value targets instantly
Adaptive threatsAttacks that evolve in real time based on the defences they encounter
Faster zero-daysAccelerated discovery and exploitation of unknown vulnerabilities
Deepfakes Are Not Hypothetical

A 2025 Gartner survey of around 300 cybersecurity leaders found roughly 62% of organisations had faced at least one deepfake attack in the prior twelve months — commonly on audio calls, and frequently on video calls. Meanwhile research consistently shows humans are poor at spotting high-quality deepfakes. The practical implication: "I recognised their voice" is no longer an authentication control. Verify high-value requests through a separate, known channel — always.

How Cybersecurity Can Use AI as a Defence

AI-driven defence works because it can analyse massive datasets and surface threats in near real time — far beyond what humans can do manually. AI can spot anomalies like unusual traffic patterns, abnormal user behaviour or suspicious file activity, and it can automate repetitive triage, freeing experts to focus on investigation and strategy. Over time, well-tuned and well-governed models learn from incidents and adapt to evolving attack methods.

The measurable payoff is significant. IBM's breach research has found that organisations using AI and automation extensively in security operations contain breaches substantially faster — on the order of 100+ days quicker — and at materially lower cost than those that don't. That is not a marketing claim; it is a defensible operational argument for AI in the SOC.

Defence Tip

Use AI to reduce noise, not to replace judgement. The winning combination is AI + human validation + strong playbooks. An AI that triages 10,000 alerts down to 40 is transformative. An AI that autonomously takes containment action with no human in the loop is a new category of risk.

The New Attack Surface: Securing AI Itself

Here is the shift many teams miss. It is not enough to defend against AI-powered attacks — you must also secure the AI systems your own organisation deploys. Every chatbot, copilot and autonomous agent expands your attack surface.

Prompt injection — where an attacker embeds malicious instructions in user input or in external data an AI reads — now sits at the top of the OWASP Top 10 for LLM Applications, and AI-specific vulnerability disclosures have risen sharply since 2022. Other core risks include sensitive information disclosure, AI supply-chain compromise, data poisoning, model theft and excessive agency.

The Governance Gap

IBM's 2025 breach research found that among breached organisations suffering an AI-related incident, the overwhelming majority lacked proper AI access controls, and a clear majority had no AI governance policy at all. This is the defining failure of the current moment: organisations are deploying AI far faster than they are governing it. "Shadow AI" — staff using unapproved tools with company data — makes it worse.

Agentic AI: the risk category for 2026

Agentic AI systems plan, act and call external tools without step-by-step human approval. That autonomy creates risks outside the scope of traditional LLM use — the OWASP Top 10 for Agentic Applications (published late 2025) highlights agent behaviour hijacking, tool misuse and exploitation, and identity and privilege abuse among the leading concerns. If an agent holds credentials and can act, compromising the agent means compromising everything it can reach. Agent identity and least privilege are now first-class security problems.

The Frameworks That Govern AI Security

The good news: you do not have to invent your approach. Three frameworks now anchor how security teams map and mitigate AI risk, alongside the emerging regulatory layer.

FrameworkTypeWhat It Gives You
OWASP Top 10 for LLM ApplicationsTechnical risk taxonomyThe vulnerability list — prompt injection, sensitive info disclosure, supply chain, excessive agency and more
MITRE ATLASAdversary knowledge baseReal-world tactics and techniques against AI systems (the ATT&CK equivalent for AI)
NIST AI RMFGovernance frameworkFour functions — Govern, Map, Measure, Manage — plus a Generative AI profile
ISO/IEC 42001Management system standardCertifiable AI management system, the ISO 27001 analogue for AI
EU AI ActRegulationRisk-tiered legal obligations with phased milestones through 2026 and beyond

If you are starting from zero, the sequence is straightforward: inventory your AI systems, assess them against the OWASP LLM Top 10, structure your programme with NIST AI RMF, and determine your EU AI Act exposure if you touch EU citizens' data — regardless of where you are based.

Ethical Considerations of Using AI in Cybersecurity

AI in security brings real ethical responsibilities that sit alongside the technical ones.

  • Bias and discrimination: models may reflect biased training data, affecting decisions unfairly
  • Privacy violations: large-scale monitoring can sweep up sensitive personal data
  • Transparency: "black box" decisions reduce trust and auditability
  • Accountability: unclear responsibility when AI makes harmful errors
  • Job displacement: automation shifts roles — teams need reskilling, not panic
  • Data security: AI depends on datasets that must be strongly protected
  • Dual use: defensive tools can be repurposed offensively
  • Fairness: controls should apply consistently without unfairly targeting groups
  • Human oversight: humans must remain responsible for high-impact decisions
  • Security of AI systems: models can be attacked, poisoned or manipulated

Building AI Security Skills

AI security is now one of the fastest-growing specialisms in the field, and the skills gap is real — recent ISC2 workforce research puts AI at the top of the skills organisations say they need most. If you want structured learning in this area, Cybernous runs a dedicated GenAI Expert Cybersecurity Professional (GAESP) programme. Complementary foundations and specialisms include:

  • Certified in Cybersecurity (CC) and CompTIA Security+ for entry-level grounding
  • CISSP and CISM for the architecture, governance and leadership layer that AI risk ultimately reports into
  • SANS and offensive security courses for hands-on AI red-teaming depth
  • University programmes offering cybersecurity and AI degrees
Where This Meets CISSP & CISM

AI does not create a separate rulebook — it stresses the existing one. CISSP's risk management, security architecture and asset security domains all apply directly to AI systems, and CISM's governance, risk and programme management framing is exactly what an AI governance policy needs. The professionals best positioned for AI security roles are usually those with strong governance fundamentals plus AI-specific knowledge — not AI enthusiasts without a security foundation.

AI and Cybersecurity: The Future Landscape

The future of cybersecurity will be shaped heavily by AI. Systems will get better at detecting subtle anomalies and handling routine response actions, and automation will accelerate incident response, letting humans focus on deeper investigation and strategy.

But the same progress fuels AI-powered attacks. Organisations must therefore build defences against AI-driven threats and develop ethical guardrails covering bias, privacy and responsible usage. Encouragingly, maturity is rising: the share of organisations assessing AI tool security before deployment has climbed sharply year over year. The gap between the leaders and the laggards, however, is widening.

Forward View

The real battlefield isn't "AI vs AI." It's governed AI vs uncontrolled AI. Teams that bake governance into detection, response and model use will win. Teams that bolt AI onto weak fundamentals will simply fail faster and at greater scale.

What to Do Next

If you take one thing from this article, make it an action list rather than an opinion:

  • Inventory your AI systems — you cannot secure what you cannot see, including shadow AI
  • Assess LLM exposure against the OWASP LLM Top 10, especially prompt injection and excessive agency
  • Write an AI governance policy — most breached organisations with AI incidents had none
  • Apply least privilege to agents — treat agent identity as seriously as human identity
  • Verify out-of-band for high-value requests; voices and faces are no longer proof of identity
  • Run an AI tabletop exercise — deepfake impersonation, compromised agent, poisoned model
  • Fix the fundamentals — identity, logging, patching. AI amplifies whatever is already there.

Build AI Security Expertise That Leads

Cybernous trains security professionals for the AI era — from the GenAI Expert (GAESP) programme to CISSP and CISM coaching that grounds AI risk in real governance. Coached by Manoj Sharma, with a 98.4% first-attempt pass rate and 2,000+ certified across 40+ countries.

Explore the GenAI Expert Programme →

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Frequently Asked Questions

Attackers use AI to increase the speed, scale and believability of their operations. The most common applications include highly personalised phishing — AI writes fluent, contextually convincing messages, which eliminates the spelling and grammar errors that used to give phishing away. They also use it for malware generation, producing polymorphic code that mutates to evade signature-based detection; deepfakes, creating synthetic audio and video for impersonation and social engineering; and automated attacks, running large-scale scanning, probing and exploitation at machine speed. AI further helps attackers with evasion techniques that bypass rule-based controls, rapid triage of stolen data to identify the highest-value targets, adaptive attacks that evolve in real time based on the defences they encounter, and faster discovery and exploitation of unknown vulnerabilities. Industry breach research indicates a meaningful and growing share of incidents now involve attacker use of AI. The important framing is that AI rarely invents entirely new attack categories — it makes existing ones faster, cheaper, more scalable and more convincing. That is precisely why weak fundamentals like poor identity controls, thin logging and slow patching become far more dangerous in the AI era.
No, and the evidence points the other way — AI is reshaping cybersecurity roles rather than eliminating them. AI genuinely excels at tasks humans do poorly: analysing enormous volumes of data, spotting statistical anomalies across millions of events, and handling repetitive triage at speed. Organisations that deploy AI and automation in security operations demonstrably contain breaches faster and at lower cost. But AI cannot own accountability, exercise contextual judgment, navigate organisational politics, make risk decisions with business trade-offs, or take legal and ethical responsibility for outcomes — all of which are central to security work, particularly at senior levels. Governance, risk and compliance functions are among the least automatable parts of the field precisely because they require accountable human judgment. What is actually happening is a shift in role composition: AI absorbs repetitive triage while humans move up the value chain into investigation, threat hunting, architecture and strategy. That shift does require reskilling — professionals who understand how to use, govern and secure AI will thrive, while those who ignore it may find their narrow, repetitive tasks automated. The winning posture is AI plus human validation plus strong playbooks, not AI instead of people.
Prompt injection is an attack where malicious instructions are embedded into the input an AI system processes, causing it to behave in ways its operators never intended — such as revealing sensitive information, ignoring its safety instructions, or taking unauthorised actions. It ranks as the top vulnerability on the OWASP Top 10 for LLM Applications, and for good reason. It comes in two broad forms: direct injection, where a user types manipulative instructions straight into a chatbot, and indirect injection, where the malicious instruction is hidden in external content the AI reads — a web page, a document, an email, or data retrieved from a knowledge base. Indirect injection is particularly dangerous because the victim may never see the malicious text; the AI encounters it while doing its job. The risk escalates dramatically with agentic AI: if an AI agent holds credentials and can call tools, a successful injection can translate into real actions on real systems. Mitigations include rigorous input validation, semantic filtering, clear boundaries between instructions and data, output filtering, least-privilege design for any tools the AI can access, and human approval gates for high-impact actions. It is the defining AI-specific vulnerability of this era.
It is serious and already widespread rather than hypothetical. A 2025 Gartner survey of around 300 cybersecurity leaders found roughly 62% of organisations had experienced at least one deepfake attack in the preceding twelve months, most commonly on audio calls and frequently on video calls. Research also consistently shows that humans are unreliable at detecting high-quality synthetic media — detection accuracy for convincing deepfake video is alarmingly low, and even automated detection tools struggle against the most advanced generation platforms. The practical business impact is fraud: attackers impersonate executives or trusted colleagues to authorise payments, request credentials or manipulate staff, and the losses can be substantial. The critical shift in thinking is this: recognising someone's voice or face is no longer an authentication control. Organisations need process-level defences rather than perception-level ones — out-of-band verification through a separate known channel for any high-value or unusual request, callback procedures using previously verified contact details, dual authorisation for significant payments, and staff training that specifically covers synthetic media. Technology alone will not solve this, because detection is losing the arms race against generation. Sound process is what actually protects you.
Three frameworks now anchor most AI security programmes, and they complement rather than compete with each other. The OWASP Top 10 for LLM Applications is the technical risk taxonomy — it enumerates the vulnerability classes you need to test for, led by prompt injection and including sensitive information disclosure, supply chain risks and excessive agency. MITRE ATLAS is the adversary knowledge base, cataloguing real-world tactics and techniques used against AI systems; think of it as the ATT&CK equivalent for AI. The NIST AI Risk Management Framework provides the governance structure, organised around four functions — Govern, Map, Measure and Manage — with a companion generative AI profile. Beyond these, ISO/IEC 42001 offers a certifiable AI management system standard (roughly the ISO 27001 analogue for AI), and the EU AI Act imposes risk-tiered legal obligations with phased milestones that matter to any organisation touching EU citizens regardless of where it is based. For agentic systems, OWASP's Top 10 for Agentic Applications addresses risks like agent behaviour hijacking and tool misuse. A practical sequence: inventory your AI systems, assess against the OWASP LLM Top 10, structure the programme with NIST AI RMF, and check your regulatory exposure.
Agentic AI refers to systems that can plan, take actions and call external tools without requiring human approval at each step. Rather than simply answering a question, an agent can browse, query databases, execute code, send messages or trigger workflows to accomplish a goal. That autonomy is exactly what makes it valuable — and exactly what makes it a distinct risk category beyond traditional LLM use. The core problem is that agents typically hold credentials and possess the ability to act, so compromising an agent means compromising everything it can reach. OWASP's Top 10 for Agentic Applications, published in late 2025, highlights agent behaviour hijacking (manipulating an agent into pursuing attacker goals), tool misuse and exploitation (abusing the agent's legitimate capabilities), and identity and privilege abuse among the leading concerns. A successful indirect prompt injection against an agent is no longer just an information leak — it becomes real action on real systems. The defensive implications are significant: agent identity must be managed as rigorously as human identity, least privilege must be applied to every tool an agent can invoke, human approval gates belong on high-impact actions, and agent activity needs comprehensive logging. For 2026, this is the AI risk category security teams most urgently need on their map.
AI is now embedded across the defensive stack rather than existing as a separate product category. Intrusion detection and prevention systems (IDPS) use AI to identify and block malicious traffic patterns that rule-based detection would miss. SIEM platforms apply AI and machine learning to correlate and analyse vast volumes of security data, accelerating detection and reducing the alert noise analysts must wade through. Endpoint detection and response (EDR) tools use AI to monitor endpoint behaviour, spot anomalies and respond automatically to known-bad activity. Vulnerability management platforms use AI to prioritise which fixes actually matter based on exploitability and business context, rather than dumping an undifferentiated list of CVEs on already-stretched teams. Beyond these, user and entity behaviour analytics (UEBA) detects abnormal user activity, and email security tools use AI to catch sophisticated phishing that signature-based filters miss. A newer and increasingly essential layer secures AI itself: model scanning, AI red-teaming platforms, output filtering and guardrail proxies, and agent identity management. The practical guidance is to use AI to reduce noise rather than replace judgment — the goal is triaging thousands of alerts down to the handful a human should genuinely investigate.
AI can meaningfully compress every phase of incident response. In detection, it identifies anomalies and correlates weak signals across huge datasets far faster than manual review, catching incidents earlier. In analysis, it rapidly assesses the scope of an attack — which systems are affected, what data was touched, how the attacker moved — work that might otherwise consume days of analyst time. In containment and remediation, it can recommend response steps and, where appropriate and governed, automate their execution, dramatically reducing dwell time and impact. The measurable benefit is well documented: IBM's breach research has found organisations using AI and automation extensively in security operations contain breaches on the order of 100+ days faster and at materially lower cost than those that do not. However, the governance caveat matters enormously. Autonomous containment without human oversight introduces its own risk — a false positive that automatically isolates a critical production system can cause damage comparable to the attack. The mature model is AI-assisted response with human validation on high-impact decisions, backed by strong, well-rehearsed playbooks. AI should compress the time to a good decision, not remove the decision-maker.
Yes, and the data on this is stark. IBM's 2025 breach research found that among breached organisations that suffered an AI-related incident, the overwhelming majority lacked proper AI access controls and a clear majority had no AI governance policy at all. This is the defining organisational failure of the current moment: AI is being deployed far faster than it is being governed. A meaningful AI governance policy should cover several things. It needs a complete inventory of AI systems in use, including "shadow AI" — the unapproved tools staff use with company data, often with good intentions and no oversight. It should define approval and procurement processes for AI tools, acceptable-use rules for staff, data-handling boundaries (what may and may not be fed into which systems), access controls for AI systems and agents, and monitoring and logging requirements. It should also assign clear accountability for AI decisions and errors. Frameworks like NIST AI RMF and ISO/IEC 42001 provide ready-made structure so you need not start from a blank page. The best approach to shadow AI is user-friendly guidance rather than blanket restriction, since heavy-handed bans simply push usage underground where it cannot be secured.
The most effective path into AI security starts with a solid security foundation rather than with AI alone. The professionals most in demand are those who understand security fundamentals — identity, architecture, risk, governance — and then add AI-specific expertise on top, not AI enthusiasts without security grounding. Begin with foundational credentials such as Certified in Cybersecurity (CC) or CompTIA Security+ if you are new to the field, and build practical experience. Then layer AI-specific knowledge: learn the OWASP Top 10 for LLM Applications thoroughly (especially prompt injection and excessive agency), study MITRE ATLAS for real-world adversary techniques against AI, and understand governance frameworks like NIST AI RMF and ISO/IEC 42001 plus the regulatory landscape including the EU AI Act. Structured programmes help — Cybernous runs a dedicated GenAI Expert Cybersecurity Professional (GAESP) course for exactly this transition. For those targeting senior and leadership roles, CISSP and CISM remain highly valuable because AI risk ultimately reports into architecture and governance functions. Get hands-on wherever possible: experiment with prompt injection in safe lab environments, review real AI deployments against the OWASP list, and practise AI red-teaming. Demand is strong — recent ISC2 workforce research places AI at the top of the skills organisations most need.

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