AI in Defence and Offensive Operations: Strategic Opportunities and Emerging Threats for the C-Suite

AI in Defence and Offensive Operations: Strategic Opportunities and Emerging Threats for the C-Suite

Executive Summary

Artificial Intelligence (AI) is rapidly transforming the battlefield—both physical and digital. For the C-Suite, especially CISOs, CTOs, and CEOs, understanding the dual-edged nature of AI in defence and offensive operations is no longer optional; it’s strategic. While AI enhances security operations through real-time detection, threat intelligence, and automated responses, it simultaneously empowers adversaries to be agile, personalise, and automate cyberattacks.

This blog provides an in-depth analysis of AI’s role across defensive and offensive cyber threats,  pragmatic use cases, real-world threat scenarios, and actionable insights to support strategic decision-making.

1. Introduction: AI’s Ascendancy in Modern Security

From national intelligence agencies to corporate security operation centres (SOCs), AI is being integrated across multiple layers of cyber defence. It helps automate mundane tasks, improve incident response times, and uncover threats that would otherwise go unnoticed. But as organisations integrate AI to defend assets, adversaries are doing the same to enhance offensive capabilities.

AI’s unbiased in intent makes it both a shield and a sword. Its potential to synergise and correlate vast data, impersonate human behaviour, and generate synthetic media gives both defenders and attackers a strategic edge.

2. AI in Defence Operations

a. Improving Office Productivity and Intelligence Workflows

AI systems such as Microsoft Copilot and Google Duet are transforming general office productivity—drafting reports, analysing data, translating communications, and scheduling operations. In a defence setting, these capabilities streamline security documentation, threat analysis, and case management.

b. AI for OSINT, Research, and Communications

Open-Source Intelligence (OSINT) collection is enhanced by AI’s capacity to scan, analyse, and summarise information from thousands of sources. Tools like Maltego and Recorded Future integrate NLP and ML to mine geopolitical data, actor profiles, and public threat advisories.

Moreover, AI improves international and cross-cultural communications through contextual translations, sentiment analysis, and culturally aware language processing—essential in multinational operations and incident response across borders.

c. Threat Detection and Automated Response

i. Intrusion Detection and Anomaly Detection

Security vendors like Darktrace use unsupervised ML to establish a baseline of normal behaviour across networks, then flag deviations as anomalies. Their Enterprise Immune System operates autonomously, capable of detecting insider threats, ransomware, and zero-day exploits without prior signature knowledge.

ii. Phishing and Email Threat Detection

Platforms like Proofpoint and Microsoft Defender incorporate AI to:

• Analyse metadata and writing style
• Detect deceptive email addresses
• Recognise domain impersonation
• Learn from user behaviour to adjust filters dynamically

These tools reduce false positives and alert fatigue while blocking real threats before they reach users.

iii. Endpoint Protection and Behavioural Analysis

AI enables real-time endpoint telemetry monitoring. CrowdStrike Falcon employs behavioural AI models trained on petabytes of endpoint activity to detect malware-less threats like credential theft and lateral movement.

iv. Microsoft Copilot for Security

By leveraging OpenAI’s GPT, Microsoft Copilot streamlines:

• Threat hunting queries
• Summary of incidents
• Recommendations for remediation This reduces analyst cognitive load and response times, and offers executives timely insights for decision-making.

3. AI in Offensive Operations

AI is democratising cybercrime. No longer confined to elite hackers, offensive capabilities powered by AI can be weaponised by less-skilled threat actors with devastating effect.

a. Enabling Malicious Efficiency

AI can boost malicious operations in areas such as:

• Generating phishing content at scale
• Translating and adapting scams across languages
• Synthesising fake social media content
• Scraping and profiling victims from public data

Like defenders, attackers also gain improved productivity and scalability.

b. AI in Social Engineering and Attack Planning

i. Reconnaissance Automation

AI tools scrape social media, forums, and dark web leaks to build target profiles for spear phishing. NLP models can summarise posts and infer personal interests, locations, and connections—reducing reconnaissance time.

ii. Generative Phishing and Deepfake Creation

AI-generated emails, voice clones, and video deepfakes allow for:

• Believable phishing attempts
• Fake CEO voice scams (vishing)
• Synthetic videos for social engineering

Attackers use large language models (LLMs) like GPT to generate variations of phishing messages that evade detection by signature-based filters.

iii. Malvertising and Content Spoofing

AI can generate convincing advertisements, landing pages, and fake news articles designed to lure users into downloading malware or surrendering credentials.

c. Offensive AI in the Wild: Notable Cases

i. MIT’s Automated Exploit Generation (AEG)

AEG showcased AI’s ability to autonomously identify vulnerabilities in software and generate working exploits—a harbinger of future autonomous attack frameworks.

ii. IBM’s DeepLocker

Demonstrated how AI could embed malware within benign software, activating only under certain conditions (e.g., recognising a target’s face using facial recognition). It represents an AI-powered malware that can evade detection until precisely the right moment.

iii. Rhadamanthys Malware-as-a-Service (MaaS)

As reported by CERT in October 2024, Rhadamanthys incorporated AI-based Optical Character Recognition (OCR) to extract sensitive text (e.g., passwords) from images and screenshots—a rare but real-world example of offensive AI deployment.

iv. Topic Modelling Attacks (2019)

Researchers presented AI-enabled methods that exploited NLP-based topic modelling to map internal networks and classify sensitive email communications, opening the door for automated surveillance and classification attacks.

d. Real-World Use Case: Executive Deepfake Voice Scam at Arup

In early 2024, the global engineering firm Arup reportedly fell victim to one of the most sophisticated AI-enabled scams to date, losing $25 million to fraudsters. The attackers used LLM-generated scripts alongside AI voice cloning to mimic a senior executive’s speech patterns and tone during a deepfake video conference.

The AI-generated voice directed employees to execute large financial transfers under the guise of urgency and confidentiality—a classic hallmark of Business Email Compromise (BEC), now evolved into Real-Time Executive Impersonation.

This incident highlights how:

• LLMs can generate convincing dialogue and business language, enhancing believability.
• AI voice cloning technologies can now fool human listeners during live interactions.
• Multi-modal deception (text + voice + video) creates an unprecedented threat level.
• Traditional verification protocols fail in high-trust, hierarchical corporate cultures.

❝This wasn’t just another phishing email. It was a live, real-time meeting with an AI-generated voice that employees trusted—and acted upon.❞

This attack echoes a broader shift where cybercriminals combine generative AI with psychological manipulation, blurring the line between digital and physical impersonation.

4. Comparative Table: Defensive vs Offensive AI Capabilities

5. Risk Mitigation Strategies for the C-Suite

a. Zero Trust and AI Audit Trails

AI decision-making must be explainable. Incorporate Explainable AI (XAI) models and ensure all AI tools log actions for post-incident reviews.

b. Human-in-the-Loop (HITL) Systems

Never fully automate high-impact decisions. Maintain human validation for:

• Critical security alerts
• AI-generated remediation steps
• Executive impersonation detection

c. Red Teaming with Offensive AI Simulations

Include adversarial AI simulations in red teaming exercises to:

• Test phishing resistance
• Identify model poisoning vulnerabilities
• Evaluate defences against deepfakes

d. Ethical AI Procurement and Governance

Ensure all AI tools:

• Comply with GDPR, NIS2, and AI Act (EU)
• Undergo regular security assessments
• Are not black-box systems with unknown training data

e. Cross-Training Security and Data Science Teams

Bridge the gap between cybersecurity and AI specialists. Establish cross-functional units to collaboratively monitor, assess, and mitigate AI-driven threats.

6. Navigating the AI Security Paradigm

AI is redefining the rules of engagement in cyber defence and offence. While the benefits for defenders are significant—automation, efficiency, and enhanced insight—so too are the opportunities for adversaries.

For the C-Suite, strategic action is required:

• Stay informed of AI trends in cyber offence and defence
• Invest in AI-literate cybersecurity talent
• Engage in continuous scenario planning to outpace threat evolution

AI is not merely a tool; it is a domain of competition. Success will be determined by agility, awareness, and alignment between business strategy and cybersecurity posture.

7. Appendix: Glossary of Key Terms

• AI (Artificial Intelligence): Simulation of human intelligence in machines.
• ML (Machine Learning): Algorithms that improve from data without being explicitly programmed.
• OSINT (Open-Source Intelligence): Publicly available data used for intelligence purposes.
• EDR (Endpoint Detection and Response): Security solutions that detect and investigate suspicious activity on endpoints.
• Deepfake: AI-generated synthetic media used to impersonate individuals.
• Explainable AI (XAI): AI whose decision-making process can be understood by humans.

✅ Executive Impersonation Defence Checklist

Protecting Leadership and Financial Operations from AI-Powered Scams

🔐 1. Implement Multi-Layered Verification for Financial Transactions

• Require dual authorisation for all financial transfers above a threshold.
• Introduce out-of-band verification (e.g. call-back on a known number, internal app confirmation).
• Block financial approvals based solely on video calls, emails, or instant messages.

🧠 2. Conduct Deepfake and AI Voice Awareness Training

• Run interactive training for executives and finance teams on AI-generated scams.
• Use mock deepfake simulations in tabletop exercises to build scepticism and response strategies.

🔍 3. Audit and Limit Public Executive Data

• Remove or redact voice/video samples of executives from public websites, panels, and webinars.
• Limit public exposure of executive travel plans, org charts, and speaking engagements.

🎙️ 4. Secure Internal Communication Channels

• Prefer company-approved communication tools with built-in identity validation (e.g., MS Teams, Zoom with MFA).
• Disable auto-join features for executive meetings; verify participants before allowing entry.

🧑‍💼 5. Protect Executive Digital Identities

• Register official executive accounts on major social and messaging platforms (LinkedIn, WhatsApp, Telegram) to reduce spoofing.
• Use AI-generated watermarking tools for executive communications and videos to detect tampering.

⚠️ 6. Create a Rapid-Response Protocol for Suspicious Executive Requests

• Develop an internal “executive impersonation alert protocol” to:
  • Pause suspicious requests
  • Notify the cybersecurity team
  • Launch quick verification workflows

🛠️ 7. Deploy Deepfake Detection Tools

• Integrate AI-powered media forensics (e.g. Truepic, Intel’s FakeCatcher, Microsoft Video Authenticator) to:
  • Analyse real-time calls
  • Flag manipulated content
  • Evaluate voice clones and video streams

📊 8. Monitor Behavioural Biometrics in Communications

• Adopt tools that detect unusual communication patterns, such as:
  • Tone inconsistencies
  • Irregular grammar or phrasing
  • Timing anomalies in typed or spoken messages

📋 9. Institute Executive-Specific Access Control

• Use role-based access to restrict who can initiate or validate high-risk actions in financial systems.
• Employ Just-In-Time (JIT) access provisioning to reduce long-term access privileges for sensitive accounts.

🔐 10. Mandate Incident Disclosure in the C-Suite

• Encourage prompt, non-punitive disclosure of any suspicious communication received by executives.

“Silence empowers deception. Transparency builds resilience.”

In many high-stakes fraud cases, delayed reporting or executive hesitation to flag suspicious activity has amplified financial and reputational damage. A formal culture of open, non-punitive disclosure is essential to counter AI-powered impersonation.

✅ Recommended Actions:

• Establish a “No Shame” Reporting Protocol:
  
  Make it clear that reporting suspected impersonation—even if false—is a leadership responsibility, not a sign of gullibility.
  
• Designate a Direct Line to Cybersecurity or Legal Teams:
  
  Create confidential channels (e.g. secure hotline, encrypted email, or executive-only Slack channel) where anomalies can be quickly flagged for investigation.
  
• Add Disclosure Metrics to Quarterly Risk Reports:
  
  Include stats like:
  
  • Number of impersonation attempts reported
    
  • Average time-to-report
    
  • Actions taken and outcomes
    
    This demonstrates C-level accountability to the board and reinforces a culture of vigilance.
    
• Integrate with Crisis Management Playbooks:
  
  Ensure that any potential executive impersonation triggers an immediate, pre-defined response protocol involving PR, legal, and cyber teams.
  
• Lead by Example:
  
  If a CEO or CFO receives a suspicious call, publicly share the incident internally—even if nothing happened. It builds psychological safety for others to do the same.